text:
Average annual gross return of 7.8%.
Bolden allows you to lend directly to established or developing French SMEs, alongside individual and institutional investors. As an individual or legal entity, you have access to Bolden Lending loans that have a term of 1 to 5 years, are amortizable and pay a fixed interest rate.
Lenders benefit from it, lending at attractive rates that remunerate the risk incurred, and being able to choose a project and a company manager according to very personal criteria.
I like the idea of supporting a company on a daily basis and above all of making a concrete contribution to the country's economy. With Bolden, I have found a profitable investment that is easy to understand and that I can control without investing too much.
You directly select the SMEs to which you lend and build the loan portfolio of your choice.
This free service allows you to automatically lend to SMEs based on predefined criteria. You can diversify your loan portfolio effortlessly.
Investing in SMEs: what are the risks?
Investing in SMEs involves risks: risk of capital loss in the event of SME default, risk of non-payment of interest, liquidity risk (immobilisation of your savings and inability to sell the loans made), as well as risks related to the borrowers' activities. The higher the rate, the greater the risk of loss.
Companies are rigorously selected by Bolden, but despite all our efforts, zero risk does not exist. We therefore recommend that you diversify your investments across several projects.
If a repayment deadline is not met, Bolden will notify you and contact the borrower to collect or reschedule the debt if necessary. If no solution is found, Bolden mandates a collection agency.
In the event of Bolden's cessation of activity, our payment service provider S-money contractually ensures the repayment by SMEs of existing loans until their maturity.
Our system for the payment and management of electronic money accounts is operated by S-money, a subsidiary of the Natixis group. S-money is an electronic money institution approved in France by the Autorité de Contrôle Prudentiel et de Résolution, under bank code (CIB) n°16528 to issue and manage electronic money and provide payment services under articles L526-1 et seq. of the CMF (Code monétaire et financier).
For an individual resident in France, the interest you receive each month is a fixed income investment product, subject to the single flat-rate withholding tax ("PFU"). Bolden withholds 30% of the interest paid to you, consisting of an income tax levy (12.8%) and social security contributions (17.2%).
You may opt, when you file your tax return, for a progressive tax rate. This is interesting if your marginal tax rate is less than 12.8%.
- less than €50,000 if you are subject to joint taxation.
Loan maturities are repaid by our Borrowers by monthly withdrawals which take place on a fixed date. Upon notification of the rejection of a drawdown, the borrower is immediately contacted to agree on the regularization of its outstanding due date, at the latest by the end of the month. At this stage, and until the end of the month, the loan is considered late.
Our Borrowers are mainly reachable within 2 days maximum and react by sending a transfer before the end of the month. If the arrears cannot be regularized before the end of the month, the loan file is considered as a payment incident (30 days).
The collection of unpaid debts is entrusted to our Collection Agency if the borrower has 2 unpaid debts (60 days), and no arrangement can be validated.
The Collection Agency will attempt amicable collection for 60 days, ensuring on the one hand that the arrears are regularized and on the other hand that current due dates are paid. In the event of failure, the loan is terminated. This means that the remaining principal due becomes immediately due in addition to unpaid installments. At this stage, the loan file is in default.
Bolden remains dependent on the delays in the proceedings in the actions initiated. These deadlines may vary from one jurisdiction to another. Defending the interests of lenders remains a major objective. Also the negotiation of a memorandum of understanding approved by the Judge may be an alternative to minimize the risk of loss, and accelerate the recovery of the claim. The memorandum of understanding consists of a reorganisation of the repayment conditions, i.e. the determination of a new schedule.
Depending on the creditworthiness of the Borrower and the assets identified, the taking of real guarantees, such as a pledge for example, may be included in the negotiation. Any amount recovered retrospectively, i.e. in the context of the judicial phase, shall be paid proportionately to all lenders on a monthly basis.
In the event of collective proceedings, the claim shall be declared to the Judicial Agent by Bolden on behalf of all the lenders it represents. The loan is considered in default at this stage. No further withdrawals can be made from the Borrower's bank account, and no direct recovery from its manager is possible.
A loan whose repayment normally occurs can therefore be classified as in default without going through the recovery phases previously described.
The probability of recovery of the debt is variable, depending on whether it is a judicial liquidation or a judicial redress. In the first case, the certificate of non-recoverability provided by the judicial representative attests to the permanent loss of the debt. In the second case, a plan is established for the repayment of debts. In the latter case, Bolden, like the other creditors, is subject to the time limits of the procedure to determine the conditions for repayment of the claim.
The events described above that will occur in the life of the file are communicated monthly to the lenders concerned. Defaulting loans subject to a longer procedure may be subject to less regular communication depending on the time taken by the courts.
Capital losses recognized on participating loans granted since January 1, 2016, may be offset against interest generated by other participating loans or minibons in the same year or the following 5 years. This capital loss allocation system is extended to minibons subscribed since 1 January 2017, up to a maximum of €8,000 for the same year.
- Possibly, the extract from the company's court liquidation judgment.
Warning: Lending money to VSEs/SMEs presents a risk of capital loss and requires that your savings be immobilized.

lowercased:
average annual gross return of 7.8%.
bolden allows you to lend directly to established or developing french smes, alongside individual and institutional investors. as an individual or legal entity, you have access to bolden lending loans that have a term of 1 to 5 years, are amortizable and pay a fixed interest rate.
lenders benefit from it, lending at attractive rates that remunerate the risk incurred, and being able to choose a project and a company manager according to very personal criteria.
i like the idea of supporting a company on a daily basis and above all of making a concrete contribution to the country's economy. with bolden, i have found a profitable investment that is easy to understand and that i can control without investing too much.
you directly select the smes to which you lend and build the loan portfolio of your choice.
this free service allows you to automatically lend to smes based on predefined criteria. you can diversify your loan portfolio effortlessly.
investing in smes: what are the risks?
investing in smes involves risks: risk of capital loss in the event of sme default, risk of non-payment of interest, liquidity risk (immobilisation of your savings and inability to sell the loans made), as well as risks related to the borrowers' activities. the higher the rate, the greater the risk of loss.
companies are rigorously selected by bolden, but despite all our efforts, zero risk does not exist. we therefore recommend that you diversify your investments across several projects.
if a repayment deadline is not met, bolden will notify you and contact the borrower to collect or reschedule the debt if necessary. if no solution is found, bolden mandates a collection agency.
in the event of bolden's cessation of activity, our payment service provider s-money contractually ensures the repayment by smes of existing loans until their maturity.
our system for the payment and management of electronic money accounts is operated by s-money, a subsidiary of the natixis group. s-money is an electronic money institution approved in france by the autorité de contrôle prudentiel et de résolution, under bank code (cib) n°16528 to issue and manage electronic money and provide payment services under articles l526-1 et seq. of the cmf (code monétaire et financier).
for an individual resident in france, the interest you receive each month is a fixed income investment product, subject to the single flat-rate withholding tax ("pfu"). bolden withholds 30% of the interest paid to you, consisting of an income tax levy (12.8%) and social security contributions (17.2%).
you may opt, when you file your tax return, for a progressive tax rate. this is interesting if your marginal tax rate is less than 12.8%.
- less than €50,000 if you are subject to joint taxation.
loan maturities are repaid by our borrowers by monthly withdrawals which take place on a fixed date. upon notification of the rejection of a drawdown, the borrower is immediately contacted to agree on the regularization of its outstanding due date, at the latest by the end of the month. at this stage, and until the end of the month, the loan is considered late.
our borrowers are mainly reachable within 2 days maximum and react by sending a transfer before the end of the month. if the arrears cannot be regularized before the end of the month, the loan file is considered as a payment incident (30 days).
the collection of unpaid debts is entrusted to our collection agency if the borrower has 2 unpaid debts (60 days), and no arrangement can be validated.
the collection agency will attempt amicable collection for 60 days, ensuring on the one hand that the arrears are regularized and on the other hand that current due dates are paid. in the event of failure, the loan is terminated. this means that the remaining principal due becomes immediately due in addition to unpaid installments. at this stage, the loan file is in default.
bolden remains dependent on the delays in the proceedings in the actions initiated. these deadlines may vary from one jurisdiction to another. defending the interests of lenders remains a major objective. also the negotiation of a memorandum of understanding approved by the judge may be an alternative to minimize the risk of loss, and accelerate the recovery of the claim. the memorandum of understanding consists of a reorganisation of the repayment conditions, i.e. the determination of a new schedule.
depending on the creditworthiness of the borrower and the assets identified, the taking of real guarantees, such as a pledge for example, may be included in the negotiation. any amount recovered retrospectively, i.e. in the context of the judicial phase, shall be paid proportionately to all lenders on a monthly basis.
in the event of collective proceedings, the claim shall be declared to the judicial agent by bolden on behalf of all the lenders it represents. the loan is considered in default at this stage. no further withdrawals can be made from the borrower's bank account, and no direct recovery from its manager is possible.
a loan whose repayment normally occurs can therefore be classified as in default without going through the recovery phases previously described.
the probability of recovery of the debt is variable, depending on whether it is a judicial liquidation or a judicial redress. in the first case, the certificate of non-recoverability provided by the judicial representative attests to the permanent loss of the debt. in the second case, a plan is established for the repayment of debts. in the latter case, bolden, like the other creditors, is subject to the time limits of the procedure to determine the conditions for repayment of the claim.
the events described above that will occur in the life of the file are communicated monthly to the lenders concerned. defaulting loans subject to a longer procedure may be subject to less regular communication depending on the time taken by the courts.
capital losses recognized on participating loans granted since january 1, 2016, may be offset against interest generated by other participating loans or minibons in the same year or the following 5 years. this capital loss allocation system is extended to minibons subscribed since 1 january 2017, up to a maximum of €8,000 for the same year.
- possibly, the extract from the company's court liquidation judgment.
warning: lending money to vses/smes presents a risk of capital loss and requires that your savings be immobilized.


text:
This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population. The method constitutes an expansion of the hybrid Genetic Programming (hGP) method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC) and Consumer Prices Index (CPI) leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD) countries shows very satisfactory forecasting performance.
Forecasting is an endogenous process intertwined with the evolution of science. Forecasting methodology is divided into two categories: qualitative and quantitative. Qualitative methods employ the judgment of experts group to produce forecasts . These procedures are mainly applied without using historical data. Quantitative forecasting methods are used when historical data are available as well as the assumption that some of the past patterns will be repeated in the future .
There is a variation of quantitative methods such as the time series forecasting which use past trend to forecast the future values of the variable and causal methods that, besides the past trend assumption, also examine the correlation of the variable with other indicators.
The adoption of innovative technologies by a society such as the mobile telecommunications adoption has been discussed and some widely used forecasting models have been proposed. The diffusion processes as well as the produced models are described in the literature [3–8].
The most commonly used diffusion models are Gompertz, Logistic, and Bass  which are dynamic models and follow a sigmoid curve against time. In order to follow the overall diffusion process of the mobile wireless penetration in time, we also employ the Bi-Logistic and LogInLog models which are described in the next section of this paper. The parameters of the models have been estimated by regression analysis with the Least Squares Method .
In addition to time response, we investigate the relationship of the produced models with some macroeconomic indicators such as GDPpC and CPI. The core work is an expansion, modification, and implementation of the hybrid Genetic Programming (hGP) method which was presented in  in terms of the insertion of new diffusion models as well as the macroeconomic indicators dependence.
The term Genetic Programming (GP) method is a generalization of the Genetic Algorithm (GA) which represents a heuristic method that employs the Darwinian principle of natural selection in finding an appropriate solution of a well-defined problem and every produced solution corresponds to a new program [10, 11].
The basic structure of the paper follows. Firstly, a brief reference to the GP method and the diffusion models are presented. The hGP technique analysis follows as well as the description of the modifications and expansion on it. The next section analyses the results of the hGP implementation. After that, we discuss the forecasting results, and, finally, the conclusion is presented.
GP was introduced by Koza in . In his work, the solution of a problem corresponds to a chromosome-program. The main difference between GP and GA is the representation of solutions. The tree-based representation is adopted by GP method, while a string of numbers represents the solution in GA methodology. The tree-based representation consists of nodes. The nodes represent functions or leaves which correspond to the terminals of the solution, such as variables or constants [12, 13].
The steps for the GP construction are generally the following. Firstly, GP produces an initial population of random programs-solutions composed of the functions and terminals of the problem. The next step iteratively performs the following substeps until a termination criterion will be satisfied: execution of each program and assignment of fitness value according to the precision of each solution . Then, GP generates a new generation of solutions by applying the operations of reproduction, crossover, and mutation. The selection of the candidate solution is performed by probability-based criteria on the fitness value. Reproduction refers to the copy of a solution to the new population. In crossover operation, the selected chromosomes are randomly combined per two and, recombining its chosen parts, generate new chromosomes (offspring) . The mutation changes a function in a chromosome structure with another function. The chromosomes of the new generation have better overall fitness value. The whole process is repeated until a termination criterion is satisfied [9, 11, 12].
Rogers  considers that the adoption of an innovative product by a society follows the diffusion process and it has the sigmoid curve format. In this paper, besides the well-known Logistic, Gompertz, and Bass models, we investigate the Bi-Logistic [14, 15] and the LogInLog which is inspired by the solution of the Dodd model in [16, 17].
A diffusion process is described by dynamic or nondynamic models according to whether the level of saturation is changing over time (“carrying capacity”)  or constant, respectively. The differential equation which describes the fundamental diffusion model follows the following formulation: where is the estimated diffusion saturation level for time and is the diffusion penetration and function is the diffusion coefficient.
The Logistic model is the solution of the differential equation (1) which describes the diffusion process. The Logistic model is described by where is the diffusion of a new product in a society, at time . Also, is a time dependent function and are constant parameters. The constant is the upper limit of the function , known as the saturation level. When time , then .
Also, a variation of (3) format is the following Gompertz II format with constant, in where, in both formats, is a time dependent function and are constant parameters .
Bass proposes that the adoption of a new product by a market consists of two major categories: innovators and imitators. The overall diffusion process starts with the innovators adoption of the new product or the innovative technology and then the imitators follow.
In (5), parameter corresponds to initial purchasers of the new technology product. Parameter is the sum of the innovators and imitators coefficients, and , respectively, . Parameter is , where is a constant and parameter is [6, 9].
In some cases, the overall life of a product, like mobile telecommunications, has many phases-generations. For this purpose we employ the Bi-Logistic curve which is the sum of two Logistic curves . So, where and . In the first generation, saturation is constant as well as of the second generation. Parameters , , , and are constants and and are the introduction time of the first and second generation, respectively [15, 17].
In this case, the saturation level is time dependent and it follows the Logistic diffusion model until the upper saturation of [15–17]. The whole diffusion process follows the Logistic model , as where and ; parameters are constants. The saturation level follows in where parameter is a constant.
This model describes the diffusion process when an innovative technology has created generations which are not clearly separated . It should be noted that this model is derived by the generalization of the solution of the Dodd model in [16, 17]. The parameters of the model are optimized by the least square regression.
The hybrid Genetic Programming method in fitting and forecasting was presented in a previous work . In this paper, the modified-hGP is presented extensively. The modified hGP implements a strategy which consists of three parts, the nonlinear regression analysis, the genetic algorithm part, and the final model selection. The flowchart of Figure 1 shows the parts of the modified hGP.
Figure 1: Flowchart of the modified hGP.
This stage of the method contains the preparation steps for the program execution process [11, 12, 18]. The first step is the function set definition. In the modified hGP, the set has two subsets for the arithmetic and mathematical functions, and , respectively, . So, , where and . It should be noted that division is zero protected for the denominator and is the natural logarithm.
In the second step, the terminal set of the variables and constants sets is defined. The variables set and , where , GDPpC, and PCIn are the variables for time, GDP per Capita, and normalized CPI, respectively, and is the randomly generated constants with domain in .
The next step is to define the fitness function for each solution. Various statistical indicators can be used for the fitness function during the evaluation process. Following the previous implementation of the hGP , two different fitness functions are used as follows.
In (9), the sum is over the time period . Also, is the real data for time and is the model’s value .
In this function, a weight is used, in order to give greater weight at the time interval near the last training data .
Finally, the maximum number of generations is defined to end the execution of the GP.
As mentioned before, the function set of the modified hGP is extended compared to hGP. Apart from the primary arithmetic functions set , a mathematical functions set has been inserted; . So, the modified hGP has simplified the chromosomes structure, Figures 2 and 3, while, at the same time, their mathematical efficiency has been improved.
Figure 2: Representation of chromosomes in modified hGP as strings.
Figure 3: Representation of chromosomes in modified hGP as parse trees.
The expressions of the randomly created solutions combine the following primary block format, whereas each part is randomly chosen. Block: .
The solutions of the initial population are the combination of random chosen functions, variables, constants, and primary blocks. Also, the optimized Logistic, Gompertz I, Gompertz II, Bass, Bi-Logistic, and LogInLog diffusion models are being inserted in the population. The parameters of the diffusion models are optimized by nonlinear regression analysis and the Levenberg-Marquardt algorithm has been used .
In modified hGP, each chromosome is a string of characters and corresponds to a program that is a possible solution to the problem . The inner representation of a string of characters is considered as a parse tree using the abstract syntax trees of Python Programming Language. For example, the chromosomes and are presented in Figures 2 and 3 as strings and parse trees, respectively .
The parse tree consists of nodes. There are two types of nodes, the terminal and nonterminal nodes. The terminal nodes (leaves) of the tree contain the variables or the constants. In contrast, the nonterminal nodes of the tree consist of the modified-hGP functions .
As stated above, the best solution is selected according to (9) for fitting and (10) for forecasting purposes. The evaluated solutions are inserted into a sorted Python’s list. The solutions that are not satisfying a precision limit criterion are removed. The remaining accepted solutions of the list are sorted according to their fitness value and they are candidates to become parents for the crossover operation or to be chosen in mutation. In Figure 4, the structure of the list is depicted. It should be noted that the problem of the solutions trapping into local optimum is solved keeping one of all the individuals having the same fitness value in the list.
Figure 4: Representation of the solutions’ sorted list in the selection operation of the modified-hGP.
In tournament selection, a number of solutions from the sorted solutions’ list are selected at random and, then, the best is chosen for the crossover or mutation operation.
In the crossover operation, two parents are randomly selected, according to the tournament selection process, from the sorted by the best fitness value solutions’ list.
In each parent solution, a crossover point is randomly chosen. The substring of each parent beginning at the crossover point is interchanged between two parents’ solutions and the children (offspring) are generated. The crossover operation is presented in Figures 5 and 6.
Figure 5: Crossover of the modified-hybrid Genetic Programming method (string representation).
Figure 6: Crossover of the modified-hybrid Genetic Programming method (parse tree representation).
In the mutation process, a solution is chosen by tournament selection from the tournament list. Once again, a string’s point, which depicts a function, is randomly chosen. The mutation replaces the chosen function from the , set, with a new random function in the solution.
The mutation operation is presented in Figures 7 and 8.
Figure 7: Mutation of the modified-hybrid Genetic Programming method (string representation).
Figure 8: Mutation of the modified-hybrid Genetic Programming method (parse tree representation).
The fitness function of each individual in the modified hGP method is the Sum of Squared Error (SSE) for the fitting process, as in (9), and the Weighted Sum of Squared Error (wSSE), as (10) presents.
The statistical indices in the modified hGP  are the Mean Absolute Percentage Error (MAPE), the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE).
In (15), , , and correspond to Fitness Function Value (wSSE for forecasting), parameters of the model, and the number of data points, respectively.
It should be noted that in the final selection process of the appropriate forecasting model we use the half of dataset before the last observed data point.
In this section, the macroeconomic indicators of Gross Domestic Product per Capita (GDPpC) and normalized Consumer Prices Index (CPI) will be presented. The GDPpC is a macroeconomic index for the productivity of a country and it could not be considered as index of personal income.
According to , the basic index of the value of the goods and services produced by a country is the Gross Domestic Product (GDP). The GDPpC indicates the living standards of the economy in a country.
In general, CPI indicates a weighted average of basic consumer goods prices. Moreover, in this study, the CPI relies on the individual consumption expenditure of households, less energy, and food consumption . It should be noted that CPI is normalized on the CPI of the year 2005. In Figure 9, the yearly GDPpC and CPI for the time period between the years 1997 and 2009 are presented.
Figure 9: Yearly growth of the GDPpC (in 100000$) and CPI (normalized on the year 2005).
It should be noted that, after the year 2008 (“economic crisis year”), the OECD’s GDPpC is decreased for 2009, but, on the other hand, the CPI has bigger tolerance.
According to , the first mobile telecommunications were introduced with analogue networks in the early 1980s, for voice transmission. The second generation (2G) mobile network (Global System for Mobile Communication, GSM) followed in the early 1990s and digital mobile networks were born with the first SMS service. In the late 1990s, enhanced digital generation (2.5G) was introduced for data services. The data services were changed from circuit switched transport (GSM) to packet data transport with General Packet Radio Services (GPRS) and, later, data rates grew with enhanced digital technologies such as Enhanced Data rates for GSM Evolution (EDGE).
Also, in 2003, the next generation (3G) of mobile networks, Universal Mobile Telecommunications System (UMTS), emerged with the first video-calls and, later, (around 2006) was upgraded to High Speed Packet Access (HSPA) with data rates of 14 Mbps in the downlink and 5.76 Mbps in the uplink. Then, HSPA was upgraded to HSPA+ with theoretically 168 Mbps and 22 Mbps for downlink and uplink, respectively, and data services as videos, mobile email, and music. In 2009, Long-Term Evolution (LTE) was launched for commercial usage, while a new generation (4G) of technology is coming . Figure 10 depicts the evolution of mobile technologies generations in parallel with the overall OECD mobile subscribers, contract, prepay, and 3G subscribers, from the year 1997 to 2009 .
Figure 10: OECD mobile subscribers (in billions).
It should be noted that the number of the mobile subscribers is growing through the technology generations evolution.
The results will be analysed in order to provide a satisfactory prediction for mobile subscribers which consist of mobile contract subscribers and mobile prepay subscribers in OECD countries, as well as mobile 3G subscribers.
This study investigates the implementation of modified hGP on four different datasets. The datasets present the total yearly number of OECD mobile subscribers, the yearly number of mobile contract subscribers, the yearly number of mobile prepay subscribers, and finally the yearly number of mobile 3G subscribers. The observation period begins from the year 1997 to 2009, which is comprised of 13 data points.
Table 1 contains the initialization parameters for the execution of the modified-hGP concerning the data sets in OECD countries.
Table 1: Initialization parameters of the modified-hGP.
The fitting performance of the first modified-hGP model for the total number of OECD subscribers, according to its fitness value (SSE), is presented in Figure 11. Also, Figure 12 presents the errors of the models in time (residuals). The relative statistical indices SSE, MAPE, MSE, RMSE, and MAE of the modified-hGP models are presented in Table 2.
Table 2: Statistical indices in fitting process of the modified-hGP model for the total number of OECD subscribers.
Figure 11: The fitting performance of the modified-hGP model for the total number of OECD subscribers.
Figure 12: Residuals of the modified-hGP model in fitting of the total number of OECD subscribers.
As one can see, this method combines different variables like GDPpC or CPI with the independent variable of time. In Table 2, the modified-hGP method achieves excellent statistical performance, showing an SSE value of .
The fitting performance and the residuals for the remaining data sets are presented in Figures 13 and 14 for contract subscribers, Figures 14 and 15 for prepay subscribers, and Figures 16 and 17 for 3G users, respectively. The relative statistical indices SSE, MAPE, MSE, RMSE, and MAE of the produced modified-hGP models are presented in Table 3 for contract, Table 4 for prepay, and Table 5 for 3G subscribers.
Table 3: Statistical indices in fitting process of the modified-hGP model for the OECD mobile contract subscribers.
Table 4: Statistical indices in fitting process of the modified-hGP model for the OECD mobile prepay subscribers.
Table 5: Statistical indices in fitting process of the modified-hGP model for the OECD 3G mobile subscribers.
Figure 13: The fitting performance of the modified-hGP model for the OECD mobile contract subscribers.
Figure 14: Residuals of the modified-hGP model in fitting for the OECD mobile contract subscribers.
Figure 15: The fitting performance of the modified-hGP model for the OECD mobile prepay subscribers.
Figure 16: Residuals of the modified-hGP model in fitting for the OECD mobile prepay subscribers.
Figure 17: The fitting performance of the modified-hGP model for the OECD 3G mobile subscribers.
It should be noted that this method combines different variables like GDPpC or CPI with the independent variable of time and a variation of diffusion models’ blocks. The performance of the model corresponds to a good enough behavior in fitting process. The error performance in fitting is depicted in Figure 14.
Finally, the modified-hGP model yields a satisfactory fitting performance. The error performance in fitting is depicted in Figure 18.
Figure 18: Residuals of the modified-hGP model in fitting for the OECD 3G mobile subscribers.
The forecasting results of the generated models by the modified-hGP method are presented in this section, as well as the combined diffusion models with the modified-hGP models. As mentioned before, the statistic indicator wSSE has been used for the forecasting process.
The initialization parameters for the execution of hGP are presented in Table 6, for the forecasting process. The forecasting method for a 2-year prediction uses 11 data points as training set of the GP method, except for the 3G training set which consists of 7 points. The forecasting performance of the modified-hGP models concerning total OECD mobile subscribers, contract, prepay, and 3G is depicted in Figures 19, 22, 24, and 25, respectively. In every graph, the forecast period window is presented in the blue rectangle.
Table 6: Initialization parameters of modified-hGP.
Figure 19: The forecasting performance of modified-hGP (forecast period—2 years ahead).
The forecasting performance of the optimized diffusion models, according to their fitness value (wSSE) for the 11 training points, is presented in Figure 20. Also, the relative statistical indices, concerning the whole dataset, of the produced forecasting modified-hGP and diffusion models are presented in Table 7.
Table 7: Statistical indices in forecasting process of the modified-hGP and diffusion models for the total number of OECD subscribers.
Figure 20: The forecasting performance of the diffusion models (forecast window—2 years ahead) for the total number of OECD subscribers.
Considering Table 7, it can be concluded that the modified-hGP method achieves good statistical indices combining some optimized diffusion models. We can see that the first hGP model achieves a wSSE value of 0.000226, while the best of diffusion models, Bi-Logistic, has a similar 0.000281. It should be noted that modified-hGP model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the GP model (see Figure 21).
Figure 21: Residuals of the modified-hGP model in forecasting (2 years window period) of the total number of OECD subscribers.
Figure 22: The forecasting performance of the modified-hGP (forecast window—2 years ahead) for the number of OECD mobile contract subscribers.
The forecasting performance of the diffusion models is presented in Figure 23. Also, the statistical indices of the produced forecasting modified-hGP and diffusion models are presented in Table 8.
Table 8: Statistical indices in forecasting process of the modified-hGP and diffusion models for the number of OECD mobile contract subscribers.
Figure 23: The forecasting performance of the diffusion models (forecast window—2 years ahead) for the number of OECD mobile contract subscribers.
Figure 24: Residuals of the modified-hGP model in forecasting (2 years window period) of the number of OECD mobile contract subscribers.
Figure 25: The forecasting performance of the modified-hGP (forecast window—2 years ahead) for the number of OECD mobile contract subscribers.
From Table 8, one can conclude that the modified-hGP method as well as diffusion models achieves good statistical indices. We can see that the hGP model achieves a wSSE value of and the Bi-Logistic 0.000116. Once again, the modified-hGP model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the GP model (see Figure 24).
The forecasting performance of the diffusion models is presented in Figure 26. Also, the statistical indices of the produced forecasting modified-hGP and diffusion models are presented in Table 9.
Table 9: Statistical indices in forecasting process of the modified-hGP and diffusion models for the number of OECD mobile prepay subscribers.
Figure 26: The forecasting performance of the diffusion models (forecast window—2 years ahead) for the number of OECD mobile prepay subscribers.
Table 9 shows that the modified-hGP method achieves a satisfactory performance. The modified-hGP model achieves a wSSE value of 0.000725 and the Bi-Logistic 0.000938. Once again, the modified-hGP model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the modified-hGP model, as Figure 27 depicts.
Figure 27: Residuals of the modified-hGP model in forecasting (2 years window period) of the number of OECD mobile prepay subscribers.
Figure 28: The forecasting performance of the modified-hGP (forecast window—2 years ahead) for the number of OECD mobile 3G subscribers.
The forecasting performance of the diffusion models is presented in Figure 29. Also, the statistical indices of the produced forecasting modified-hGP and diffusion models are presented in Table 10.
Table 10: Statistical indices in forecasting process of the modified-hGP and diffusion models for the number of OECD mobile 3G subscribers.
Figure 29: The forecasting performance of the diffusion models (forecast window—2 years ahead) for the number of OECD mobile 3G subscribers.
Table 10 shows that the modified-hGP method achieves a good performance. The modified-hGP model achieves a wSSE value of similar to the Logistic and LogInLog. The MAPE indicator has the specific performance cause of the initial error at the first data point. As Figure 30 depicts, the modified-hGP model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the modified-hGP model.
Figure 30: Residuals of the modified-hGP model in forecasting (2 years window period) of the number of OECD mobile 3G subscribers.
The forecasting results of the generated models by the modified-hGP method are compared with those of the ARIMA method derived. As mentioned before, the statistic indicator wSSE has been used for the forecasting process.
The operator stands for the order of the autoregressive part and operator for the degree of the derivative of part and the is order of the moving average part of (16). The are the parameters of the autoregressive part of the model, the are the parameters of the moving average part, and are the errors .
The ARIMA models that are derived by the implementation of the “Gretl, Gnu Regression, Econometrics and Time-series Library” for the aforementioned datasets are depicted below. The forecasting performance of the same modified-hGP models and ARIMA models concerning total OECD mobile subscribers, contract, prepay, and 3G, is depicted in Figures 31, 32, 33, and 34, respectively. In every graph, the forecast period window is presented in the blue rectangle. In Tables 11, 12, 13, and 14, the comparison results of the statistical indices MAPE, MSE, RMSE, and MAE for the two predicted points are presented.
Table 11: Statistical indices in forecasting process of the modified-hGP and ARIMA model for the total number of OECD subscribers.
Table 12: Statistical indices in forecasting process of the modified-hGP and ARIMA model for the total number of OECD contract mobile subscribers.
Table 13: Statistical indices in forecasting process of the modified-hGP and ARIMA model for the total number of OECD prepay mobile subscribers.
Table 14: Statistical indices in forecasting process of the modified-hGP and ARIMA model for the total number of OECD 3G mobile subscribers.
Figure 31: Comparison of the forecasting performances of modified-hGP with ARIMA model (forecast period—2 years ahead).
Figure 32: Comparison of the forecasting performances of modified-hGP with ARIMA model (forecast window—2 years ahead) for the number of OECD mobile contract subscribers.
Figure 33: Comparison of the forecasting performances of modified-hGP with ARIMA model (forecast window—2 years ahead) for the number of OECD mobile prepay subscribers.
Figure 34: Comparison of the forecasting performances of modified-hGP with ARIMA model (forecast window—2 years ahead) for the number of OECD 3G mobile subscribers.
Considering Table 11, it can be concluded that the modified-hGP method achieves better forecasting performance than ARIMA model.
From Table 12, one realizes that the hGP method presents better performance than ARIMA model.
Also, in Tables 13 and 14, the modified-hGP method achieves better forecasting statistics than ARIMA model.
It could be concluded that the overall performance of the modified-hGP achieves better statistic indices than ARIMA model for the predicted data points.
The proposed method has been tested for the stability and the robustness. The program was executed 20 times in the same dataset of the mobile subscribers. The mean gap between the best and worst solutions was decreasing as the generation was increasing. Also, the curve of the total average of fitness value per generation was decreasing. It should be noted that in Table 15 the program parameters for the testing process are presented. In Figure 35, the mean value for the fitness value for the best and worst solutions (mean gap) per generation of the testing modified-hGP and the average fitness value per generation for the program executions are presented.
Table 15: The hGP parameters for the testing process.
Figure 35: Testing the robustness of the forecasting modified-hGP.
The difference between the worst and the best solutions is decreasing. In particular, after the 25th generation, the indices above are converging. The mean gap of wSSE for the worst-best solution begins from value 0.004146752 and ends up to 0.000165101.
The introduction of GDPpC and CPI outside the time variable leads to the creation of causal forecasting models. This method provides a scenario based approach to forecasting. In order to study the future of mobile subscription in OECD countries, three scenarios are presented, according the GDPpC and CPI growth.
The pessimistic one concerns a continuing crisis scenario, so that the GDPpC and CPI growth rates are not increased. The second is a moderate growth scenario and the last one is the optimistic scenario, with GDPpC and CPI getting increased.
A variation of models is generated by the implementation of the modified-hGP method. According to Bayesian’s criterion as well as the wSSE criterion, two models which combine all the variables, GDPpC, CPI, and time, are chosen. Figures 36, 37, and 38 depict the pessimistic, moderate, and optimistic scenario, respectively.
Figure 36: The forecasting performance (5 years ahead) of modified-hGP models—the pessimistic scenario.
Figure 37: The forecasting performance (5 years ahead) of modified-hGP models—the moderate scenario.
Figure 38: The forecasting performance (5 years ahead) of modified-hGP models—the optimistic scenario.
In Table 16, the selected models and their statistics are presented.
Table 16: Models’ expression and statistical indices of two modified-hGP models in forecasting the total number of OECD subscribers.
The BIC depends on the number of the parameters. The generated models with one variable, like time dependent models, in many cases, have better BIC performance, but not always better forecasting performance.
In contrary, multivariable models, with good enough BIC, yield a good enough forecasting performance.
In the pessimistic scenario, the first model (most likely) achieves 1.538472 billion of OECD mobile subscribers, in the year 2014. The second has a total number of 1.415613 billion subscribers. It should be noted that the GDPpC and CPI growths are unchanged.
In the moderate scenario (the most likely scenario), the first model achieves 1.8 billion of OECD mobile subscribers, in the year 2014. The second has a total number of 1.58 billion subscribers. It should be noted that the average GDPpC rate is 2.5% and average CPI rate growth 1.7%.
In the optimistic scenario, the first model achieves 1.948 billion of OECD mobile subscribers, in the year 2014. The second has a total number of 1.686 billion subscribers. It should be noted that the average GDPpC rate is about 4.4% and average CPI rate growth 2.2%.
This paper is a modification of our previous work  where the dataset was bigger, but in different area of interest. In this paper, an improved-hGP method was presented. The improved program achieved interesting forecasting models with more variables than one. This GP method was implemented in dataset concerning the mobile subscribers of the OECD countries. The forecasting performance of the modified-hGP as well as the diffusion and ARIMA models was presented and the method presented satisfactory statistical indices.
The proposed method differs from the hGP in some points. Firstly, the diffusion models’ set is extended with Bi-Logistic and LogInLog except for Logistic, Gompertz, and Bass so that the forecast horizon is improved, for long-term forecasting. Also, the functions’ set of the method is extended by the insertion of new functions and function blocks. According to this technique, chromosomes with complicated syntax expressions can be presented with short length expression stings. The tournament selection is implemented for the crossover and mutation operations in order to maximize the algorithm’s efficiency. Finally, a Bayesian inspired criterion has been implemented which, in combination with wSSE, improves the final selection of the forecasting models.
In general, the method could be considered as a forecasting tool that produces time dependent models and causal models for long-term forecasting with more variables than one. It should be noted that this method is compared with ARIMA model and achieved satisfactory performance. Also, the robustness of the proposed method has been analyzed. The implementation of the method is going to be continued on more datasets and it will be compared with other prediction methods in future work.
The authors wish to express their acknowledgments to Professor Imed Kacem, University of Lorraine, France, for his constructive comments and suggestions, which helped to improve the quality of this paper.

lowercased:
this paper proposes a modified genetic programming method for forecasting the mobile telecommunications subscribers’ population. the method constitutes an expansion of the hybrid genetic programming (hgp) method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the logistic, gompertz, and bass, as well as the bi-logistic and loginlog. in addition, the aforementioned functions and models expand the function set of hgp. the application of the method in combination with macroeconomic indicators such as gross domestic product per capita (gdppc) and consumer prices index (cpi) leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. the forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. the implementation of the modified-hgp in the datasets of mobile subscribers in the organisation for economic cooperation and development (oecd) countries shows very satisfactory forecasting performance.
forecasting is an endogenous process intertwined with the evolution of science. forecasting methodology is divided into two categories: qualitative and quantitative. qualitative methods employ the judgment of experts group to produce forecasts . these procedures are mainly applied without using historical data. quantitative forecasting methods are used when historical data are available as well as the assumption that some of the past patterns will be repeated in the future .
there is a variation of quantitative methods such as the time series forecasting which use past trend to forecast the future values of the variable and causal methods that, besides the past trend assumption, also examine the correlation of the variable with other indicators.
the adoption of innovative technologies by a society such as the mobile telecommunications adoption has been discussed and some widely used forecasting models have been proposed. the diffusion processes as well as the produced models are described in the literature [3–8].
the most commonly used diffusion models are gompertz, logistic, and bass  which are dynamic models and follow a sigmoid curve against time. in order to follow the overall diffusion process of the mobile wireless penetration in time, we also employ the bi-logistic and loginlog models which are described in the next section of this paper. the parameters of the models have been estimated by regression analysis with the least squares method .
in addition to time response, we investigate the relationship of the produced models with some macroeconomic indicators such as gdppc and cpi. the core work is an expansion, modification, and implementation of the hybrid genetic programming (hgp) method which was presented in  in terms of the insertion of new diffusion models as well as the macroeconomic indicators dependence.
the term genetic programming (gp) method is a generalization of the genetic algorithm (ga) which represents a heuristic method that employs the darwinian principle of natural selection in finding an appropriate solution of a well-defined problem and every produced solution corresponds to a new program [10, 11].
the basic structure of the paper follows. firstly, a brief reference to the gp method and the diffusion models are presented. the hgp technique analysis follows as well as the description of the modifications and expansion on it. the next section analyses the results of the hgp implementation. after that, we discuss the forecasting results, and, finally, the conclusion is presented.
gp was introduced by koza in . in his work, the solution of a problem corresponds to a chromosome-program. the main difference between gp and ga is the representation of solutions. the tree-based representation is adopted by gp method, while a string of numbers represents the solution in ga methodology. the tree-based representation consists of nodes. the nodes represent functions or leaves which correspond to the terminals of the solution, such as variables or constants [12, 13].
the steps for the gp construction are generally the following. firstly, gp produces an initial population of random programs-solutions composed of the functions and terminals of the problem. the next step iteratively performs the following substeps until a termination criterion will be satisfied: execution of each program and assignment of fitness value according to the precision of each solution . then, gp generates a new generation of solutions by applying the operations of reproduction, crossover, and mutation. the selection of the candidate solution is performed by probability-based criteria on the fitness value. reproduction refers to the copy of a solution to the new population. in crossover operation, the selected chromosomes are randomly combined per two and, recombining its chosen parts, generate new chromosomes (offspring) . the mutation changes a function in a chromosome structure with another function. the chromosomes of the new generation have better overall fitness value. the whole process is repeated until a termination criterion is satisfied [9, 11, 12].
rogers  considers that the adoption of an innovative product by a society follows the diffusion process and it has the sigmoid curve format. in this paper, besides the well-known logistic, gompertz, and bass models, we investigate the bi-logistic [14, 15] and the loginlog which is inspired by the solution of the dodd model in [16, 17].
a diffusion process is described by dynamic or nondynamic models according to whether the level of saturation is changing over time (“carrying capacity”)  or constant, respectively. the differential equation which describes the fundamental diffusion model follows the following formulation: where is the estimated diffusion saturation level for time and is the diffusion penetration and function is the diffusion coefficient.
the logistic model is the solution of the differential equation (1) which describes the diffusion process. the logistic model is described by where is the diffusion of a new product in a society, at time . also, is a time dependent function and are constant parameters. the constant is the upper limit of the function , known as the saturation level. when time , then .
also, a variation of (3) format is the following gompertz ii format with constant, in where, in both formats, is a time dependent function and are constant parameters .
bass proposes that the adoption of a new product by a market consists of two major categories: innovators and imitators. the overall diffusion process starts with the innovators adoption of the new product or the innovative technology and then the imitators follow.
in (5), parameter corresponds to initial purchasers of the new technology product. parameter is the sum of the innovators and imitators coefficients, and , respectively, . parameter is , where is a constant and parameter is [6, 9].
in some cases, the overall life of a product, like mobile telecommunications, has many phases-generations. for this purpose we employ the bi-logistic curve which is the sum of two logistic curves . so, where and . in the first generation, saturation is constant as well as of the second generation. parameters , , , and are constants and and are the introduction time of the first and second generation, respectively [15, 17].
in this case, the saturation level is time dependent and it follows the logistic diffusion model until the upper saturation of [15–17]. the whole diffusion process follows the logistic model , as where and ; parameters are constants. the saturation level follows in where parameter is a constant.
this model describes the diffusion process when an innovative technology has created generations which are not clearly separated . it should be noted that this model is derived by the generalization of the solution of the dodd model in [16, 17]. the parameters of the model are optimized by the least square regression.
the hybrid genetic programming method in fitting and forecasting was presented in a previous work . in this paper, the modified-hgp is presented extensively. the modified hgp implements a strategy which consists of three parts, the nonlinear regression analysis, the genetic algorithm part, and the final model selection. the flowchart of figure 1 shows the parts of the modified hgp.
figure 1: flowchart of the modified hgp.
this stage of the method contains the preparation steps for the program execution process [11, 12, 18]. the first step is the function set definition. in the modified hgp, the set has two subsets for the arithmetic and mathematical functions, and , respectively, . so, , where and . it should be noted that division is zero protected for the denominator and is the natural logarithm.
in the second step, the terminal set of the variables and constants sets is defined. the variables set and , where , gdppc, and pcin are the variables for time, gdp per capita, and normalized cpi, respectively, and is the randomly generated constants with domain in .
the next step is to define the fitness function for each solution. various statistical indicators can be used for the fitness function during the evaluation process. following the previous implementation of the hgp , two different fitness functions are used as follows.
in (9), the sum is over the time period . also, is the real data for time and is the model’s value .
in this function, a weight is used, in order to give greater weight at the time interval near the last training data .
finally, the maximum number of generations is defined to end the execution of the gp.
as mentioned before, the function set of the modified hgp is extended compared to hgp. apart from the primary arithmetic functions set , a mathematical functions set has been inserted; . so, the modified hgp has simplified the chromosomes structure, figures 2 and 3, while, at the same time, their mathematical efficiency has been improved.
figure 2: representation of chromosomes in modified hgp as strings.
figure 3: representation of chromosomes in modified hgp as parse trees.
the expressions of the randomly created solutions combine the following primary block format, whereas each part is randomly chosen. block: .
the solutions of the initial population are the combination of random chosen functions, variables, constants, and primary blocks. also, the optimized logistic, gompertz i, gompertz ii, bass, bi-logistic, and loginlog diffusion models are being inserted in the population. the parameters of the diffusion models are optimized by nonlinear regression analysis and the levenberg-marquardt algorithm has been used .
in modified hgp, each chromosome is a string of characters and corresponds to a program that is a possible solution to the problem . the inner representation of a string of characters is considered as a parse tree using the abstract syntax trees of python programming language. for example, the chromosomes and are presented in figures 2 and 3 as strings and parse trees, respectively .
the parse tree consists of nodes. there are two types of nodes, the terminal and nonterminal nodes. the terminal nodes (leaves) of the tree contain the variables or the constants. in contrast, the nonterminal nodes of the tree consist of the modified-hgp functions .
as stated above, the best solution is selected according to (9) for fitting and (10) for forecasting purposes. the evaluated solutions are inserted into a sorted python’s list. the solutions that are not satisfying a precision limit criterion are removed. the remaining accepted solutions of the list are sorted according to their fitness value and they are candidates to become parents for the crossover operation or to be chosen in mutation. in figure 4, the structure of the list is depicted. it should be noted that the problem of the solutions trapping into local optimum is solved keeping one of all the individuals having the same fitness value in the list.
figure 4: representation of the solutions’ sorted list in the selection operation of the modified-hgp.
in tournament selection, a number of solutions from the sorted solutions’ list are selected at random and, then, the best is chosen for the crossover or mutation operation.
in the crossover operation, two parents are randomly selected, according to the tournament selection process, from the sorted by the best fitness value solutions’ list.
in each parent solution, a crossover point is randomly chosen. the substring of each parent beginning at the crossover point is interchanged between two parents’ solutions and the children (offspring) are generated. the crossover operation is presented in figures 5 and 6.
figure 5: crossover of the modified-hybrid genetic programming method (string representation).
figure 6: crossover of the modified-hybrid genetic programming method (parse tree representation).
in the mutation process, a solution is chosen by tournament selection from the tournament list. once again, a string’s point, which depicts a function, is randomly chosen. the mutation replaces the chosen function from the , set, with a new random function in the solution.
the mutation operation is presented in figures 7 and 8.
figure 7: mutation of the modified-hybrid genetic programming method (string representation).
figure 8: mutation of the modified-hybrid genetic programming method (parse tree representation).
the fitness function of each individual in the modified hgp method is the sum of squared error (sse) for the fitting process, as in (9), and the weighted sum of squared error (wsse), as (10) presents.
the statistical indices in the modified hgp  are the mean absolute percentage error (mape), the mean square error (mse), the mean absolute error (mae), and the root mean square error (rmse).
in (15), , , and correspond to fitness function value (wsse for forecasting), parameters of the model, and the number of data points, respectively.
it should be noted that in the final selection process of the appropriate forecasting model we use the half of dataset before the last observed data point.
in this section, the macroeconomic indicators of gross domestic product per capita (gdppc) and normalized consumer prices index (cpi) will be presented. the gdppc is a macroeconomic index for the productivity of a country and it could not be considered as index of personal income.
according to , the basic index of the value of the goods and services produced by a country is the gross domestic product (gdp). the gdppc indicates the living standards of the economy in a country.
in general, cpi indicates a weighted average of basic consumer goods prices. moreover, in this study, the cpi relies on the individual consumption expenditure of households, less energy, and food consumption . it should be noted that cpi is normalized on the cpi of the year 2005. in figure 9, the yearly gdppc and cpi for the time period between the years 1997 and 2009 are presented.
figure 9: yearly growth of the gdppc (in 100000$) and cpi (normalized on the year 2005).
it should be noted that, after the year 2008 (“economic crisis year”), the oecd’s gdppc is decreased for 2009, but, on the other hand, the cpi has bigger tolerance.
according to , the first mobile telecommunications were introduced with analogue networks in the early 1980s, for voice transmission. the second generation (2g) mobile network (global system for mobile communication, gsm) followed in the early 1990s and digital mobile networks were born with the first sms service. in the late 1990s, enhanced digital generation (2.5g) was introduced for data services. the data services were changed from circuit switched transport (gsm) to packet data transport with general packet radio services (gprs) and, later, data rates grew with enhanced digital technologies such as enhanced data rates for gsm evolution (edge).
also, in 2003, the next generation (3g) of mobile networks, universal mobile telecommunications system (umts), emerged with the first video-calls and, later, (around 2006) was upgraded to high speed packet access (hspa) with data rates of 14 mbps in the downlink and 5.76 mbps in the uplink. then, hspa was upgraded to hspa+ with theoretically 168 mbps and 22 mbps for downlink and uplink, respectively, and data services as videos, mobile email, and music. in 2009, long-term evolution (lte) was launched for commercial usage, while a new generation (4g) of technology is coming . figure 10 depicts the evolution of mobile technologies generations in parallel with the overall oecd mobile subscribers, contract, prepay, and 3g subscribers, from the year 1997 to 2009 .
figure 10: oecd mobile subscribers (in billions).
it should be noted that the number of the mobile subscribers is growing through the technology generations evolution.
the results will be analysed in order to provide a satisfactory prediction for mobile subscribers which consist of mobile contract subscribers and mobile prepay subscribers in oecd countries, as well as mobile 3g subscribers.
this study investigates the implementation of modified hgp on four different datasets. the datasets present the total yearly number of oecd mobile subscribers, the yearly number of mobile contract subscribers, the yearly number of mobile prepay subscribers, and finally the yearly number of mobile 3g subscribers. the observation period begins from the year 1997 to 2009, which is comprised of 13 data points.
table 1 contains the initialization parameters for the execution of the modified-hgp concerning the data sets in oecd countries.
table 1: initialization parameters of the modified-hgp.
the fitting performance of the first modified-hgp model for the total number of oecd subscribers, according to its fitness value (sse), is presented in figure 11. also, figure 12 presents the errors of the models in time (residuals). the relative statistical indices sse, mape, mse, rmse, and mae of the modified-hgp models are presented in table 2.
table 2: statistical indices in fitting process of the modified-hgp model for the total number of oecd subscribers.
figure 11: the fitting performance of the modified-hgp model for the total number of oecd subscribers.
figure 12: residuals of the modified-hgp model in fitting of the total number of oecd subscribers.
as one can see, this method combines different variables like gdppc or cpi with the independent variable of time. in table 2, the modified-hgp method achieves excellent statistical performance, showing an sse value of .
the fitting performance and the residuals for the remaining data sets are presented in figures 13 and 14 for contract subscribers, figures 14 and 15 for prepay subscribers, and figures 16 and 17 for 3g users, respectively. the relative statistical indices sse, mape, mse, rmse, and mae of the produced modified-hgp models are presented in table 3 for contract, table 4 for prepay, and table 5 for 3g subscribers.
table 3: statistical indices in fitting process of the modified-hgp model for the oecd mobile contract subscribers.
table 4: statistical indices in fitting process of the modified-hgp model for the oecd mobile prepay subscribers.
table 5: statistical indices in fitting process of the modified-hgp model for the oecd 3g mobile subscribers.
figure 13: the fitting performance of the modified-hgp model for the oecd mobile contract subscribers.
figure 14: residuals of the modified-hgp model in fitting for the oecd mobile contract subscribers.
figure 15: the fitting performance of the modified-hgp model for the oecd mobile prepay subscribers.
figure 16: residuals of the modified-hgp model in fitting for the oecd mobile prepay subscribers.
figure 17: the fitting performance of the modified-hgp model for the oecd 3g mobile subscribers.
it should be noted that this method combines different variables like gdppc or cpi with the independent variable of time and a variation of diffusion models’ blocks. the performance of the model corresponds to a good enough behavior in fitting process. the error performance in fitting is depicted in figure 14.
finally, the modified-hgp model yields a satisfactory fitting performance. the error performance in fitting is depicted in figure 18.
figure 18: residuals of the modified-hgp model in fitting for the oecd 3g mobile subscribers.
the forecasting results of the generated models by the modified-hgp method are presented in this section, as well as the combined diffusion models with the modified-hgp models. as mentioned before, the statistic indicator wsse has been used for the forecasting process.
the initialization parameters for the execution of hgp are presented in table 6, for the forecasting process. the forecasting method for a 2-year prediction uses 11 data points as training set of the gp method, except for the 3g training set which consists of 7 points. the forecasting performance of the modified-hgp models concerning total oecd mobile subscribers, contract, prepay, and 3g is depicted in figures 19, 22, 24, and 25, respectively. in every graph, the forecast period window is presented in the blue rectangle.
table 6: initialization parameters of modified-hgp.
figure 19: the forecasting performance of modified-hgp (forecast period—2 years ahead).
the forecasting performance of the optimized diffusion models, according to their fitness value (wsse) for the 11 training points, is presented in figure 20. also, the relative statistical indices, concerning the whole dataset, of the produced forecasting modified-hgp and diffusion models are presented in table 7.
table 7: statistical indices in forecasting process of the modified-hgp and diffusion models for the total number of oecd subscribers.
figure 20: the forecasting performance of the diffusion models (forecast window—2 years ahead) for the total number of oecd subscribers.
considering table 7, it can be concluded that the modified-hgp method achieves good statistical indices combining some optimized diffusion models. we can see that the first hgp model achieves a wsse value of 0.000226, while the best of diffusion models, bi-logistic, has a similar 0.000281. it should be noted that modified-hgp model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the gp model (see figure 21).
figure 21: residuals of the modified-hgp model in forecasting (2 years window period) of the total number of oecd subscribers.
figure 22: the forecasting performance of the modified-hgp (forecast window—2 years ahead) for the number of oecd mobile contract subscribers.
the forecasting performance of the diffusion models is presented in figure 23. also, the statistical indices of the produced forecasting modified-hgp and diffusion models are presented in table 8.
table 8: statistical indices in forecasting process of the modified-hgp and diffusion models for the number of oecd mobile contract subscribers.
figure 23: the forecasting performance of the diffusion models (forecast window—2 years ahead) for the number of oecd mobile contract subscribers.
figure 24: residuals of the modified-hgp model in forecasting (2 years window period) of the number of oecd mobile contract subscribers.
figure 25: the forecasting performance of the modified-hgp (forecast window—2 years ahead) for the number of oecd mobile contract subscribers.
from table 8, one can conclude that the modified-hgp method as well as diffusion models achieves good statistical indices. we can see that the hgp model achieves a wsse value of and the bi-logistic 0.000116. once again, the modified-hgp model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the gp model (see figure 24).
the forecasting performance of the diffusion models is presented in figure 26. also, the statistical indices of the produced forecasting modified-hgp and diffusion models are presented in table 9.
table 9: statistical indices in forecasting process of the modified-hgp and diffusion models for the number of oecd mobile prepay subscribers.
figure 26: the forecasting performance of the diffusion models (forecast window—2 years ahead) for the number of oecd mobile prepay subscribers.
table 9 shows that the modified-hgp method achieves a satisfactory performance. the modified-hgp model achieves a wsse value of 0.000725 and the bi-logistic 0.000938. once again, the modified-hgp model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the modified-hgp model, as figure 27 depicts.
figure 27: residuals of the modified-hgp model in forecasting (2 years window period) of the number of oecd mobile prepay subscribers.
figure 28: the forecasting performance of the modified-hgp (forecast window—2 years ahead) for the number of oecd mobile 3g subscribers.
the forecasting performance of the diffusion models is presented in figure 29. also, the statistical indices of the produced forecasting modified-hgp and diffusion models are presented in table 10.
table 10: statistical indices in forecasting process of the modified-hgp and diffusion models for the number of oecd mobile 3g subscribers.
figure 29: the forecasting performance of the diffusion models (forecast window—2 years ahead) for the number of oecd mobile 3g subscribers.
table 10 shows that the modified-hgp method achieves a good performance. the modified-hgp model achieves a wsse value of similar to the logistic and loginlog. the mape indicator has the specific performance cause of the initial error at the first data point. as figure 30 depicts, the modified-hgp model residuals against time (data points), especially for the 2 last data points (the forecast period), show the error response of the modified-hgp model.
figure 30: residuals of the modified-hgp model in forecasting (2 years window period) of the number of oecd mobile 3g subscribers.
the forecasting results of the generated models by the modified-hgp method are compared with those of the arima method derived. as mentioned before, the statistic indicator wsse has been used for the forecasting process.
the operator stands for the order of the autoregressive part and operator for the degree of the derivative of part and the is order of the moving average part of (16). the are the parameters of the autoregressive part of the model, the are the parameters of the moving average part, and are the errors .
the arima models that are derived by the implementation of the “gretl, gnu regression, econometrics and time-series library” for the aforementioned datasets are depicted below. the forecasting performance of the same modified-hgp models and arima models concerning total oecd mobile subscribers, contract, prepay, and 3g, is depicted in figures 31, 32, 33, and 34, respectively. in every graph, the forecast period window is presented in the blue rectangle. in tables 11, 12, 13, and 14, the comparison results of the statistical indices mape, mse, rmse, and mae for the two predicted points are presented.
table 11: statistical indices in forecasting process of the modified-hgp and arima model for the total number of oecd subscribers.
table 12: statistical indices in forecasting process of the modified-hgp and arima model for the total number of oecd contract mobile subscribers.
table 13: statistical indices in forecasting process of the modified-hgp and arima model for the total number of oecd prepay mobile subscribers.
table 14: statistical indices in forecasting process of the modified-hgp and arima model for the total number of oecd 3g mobile subscribers.
figure 31: comparison of the forecasting performances of modified-hgp with arima model (forecast period—2 years ahead).
figure 32: comparison of the forecasting performances of modified-hgp with arima model (forecast window—2 years ahead) for the number of oecd mobile contract subscribers.
figure 33: comparison of the forecasting performances of modified-hgp with arima model (forecast window—2 years ahead) for the number of oecd mobile prepay subscribers.
figure 34: comparison of the forecasting performances of modified-hgp with arima model (forecast window—2 years ahead) for the number of oecd 3g mobile subscribers.
considering table 11, it can be concluded that the modified-hgp method achieves better forecasting performance than arima model.
from table 12, one realizes that the hgp method presents better performance than arima model.
also, in tables 13 and 14, the modified-hgp method achieves better forecasting statistics than arima model.
it could be concluded that the overall performance of the modified-hgp achieves better statistic indices than arima model for the predicted data points.
the proposed method has been tested for the stability and the robustness. the program was executed 20 times in the same dataset of the mobile subscribers. the mean gap between the best and worst solutions was decreasing as the generation was increasing. also, the curve of the total average of fitness value per generation was decreasing. it should be noted that in table 15 the program parameters for the testing process are presented. in figure 35, the mean value for the fitness value for the best and worst solutions (mean gap) per generation of the testing modified-hgp and the average fitness value per generation for the program executions are presented.
table 15: the hgp parameters for the testing process.
figure 35: testing the robustness of the forecasting modified-hgp.
the difference between the worst and the best solutions is decreasing. in particular, after the 25th generation, the indices above are converging. the mean gap of wsse for the worst-best solution begins from value 0.004146752 and ends up to 0.000165101.
the introduction of gdppc and cpi outside the time variable leads to the creation of causal forecasting models. this method provides a scenario based approach to forecasting. in order to study the future of mobile subscription in oecd countries, three scenarios are presented, according the gdppc and cpi growth.
the pessimistic one concerns a continuing crisis scenario, so that the gdppc and cpi growth rates are not increased. the second is a moderate growth scenario and the last one is the optimistic scenario, with gdppc and cpi getting increased.
a variation of models is generated by the implementation of the modified-hgp method. according to bayesian’s criterion as well as the wsse criterion, two models which combine all the variables, gdppc, cpi, and time, are chosen. figures 36, 37, and 38 depict the pessimistic, moderate, and optimistic scenario, respectively.
figure 36: the forecasting performance (5 years ahead) of modified-hgp models—the pessimistic scenario.
figure 37: the forecasting performance (5 years ahead) of modified-hgp models—the moderate scenario.
figure 38: the forecasting performance (5 years ahead) of modified-hgp models—the optimistic scenario.
in table 16, the selected models and their statistics are presented.
table 16: models’ expression and statistical indices of two modified-hgp models in forecasting the total number of oecd subscribers.
the bic depends on the number of the parameters. the generated models with one variable, like time dependent models, in many cases, have better bic performance, but not always better forecasting performance.
in contrary, multivariable models, with good enough bic, yield a good enough forecasting performance.
in the pessimistic scenario, the first model (most likely) achieves 1.538472 billion of oecd mobile subscribers, in the year 2014. the second has a total number of 1.415613 billion subscribers. it should be noted that the gdppc and cpi growths are unchanged.
in the moderate scenario (the most likely scenario), the first model achieves 1.8 billion of oecd mobile subscribers, in the year 2014. the second has a total number of 1.58 billion subscribers. it should be noted that the average gdppc rate is 2.5% and average cpi rate growth 1.7%.
in the optimistic scenario, the first model achieves 1.948 billion of oecd mobile subscribers, in the year 2014. the second has a total number of 1.686 billion subscribers. it should be noted that the average gdppc rate is about 4.4% and average cpi rate growth 2.2%.
this paper is a modification of our previous work  where the dataset was bigger, but in different area of interest. in this paper, an improved-hgp method was presented. the improved program achieved interesting forecasting models with more variables than one. this gp method was implemented in dataset concerning the mobile subscribers of the oecd countries. the forecasting performance of the modified-hgp as well as the diffusion and arima models was presented and the method presented satisfactory statistical indices.
the proposed method differs from the hgp in some points. firstly, the diffusion models’ set is extended with bi-logistic and loginlog except for logistic, gompertz, and bass so that the forecast horizon is improved, for long-term forecasting. also, the functions’ set of the method is extended by the insertion of new functions and function blocks. according to this technique, chromosomes with complicated syntax expressions can be presented with short length expression stings. the tournament selection is implemented for the crossover and mutation operations in order to maximize the algorithm’s efficiency. finally, a bayesian inspired criterion has been implemented which, in combination with wsse, improves the final selection of the forecasting models.
in general, the method could be considered as a forecasting tool that produces time dependent models and causal models for long-term forecasting with more variables than one. it should be noted that this method is compared with arima model and achieved satisfactory performance. also, the robustness of the proposed method has been analyzed. the implementation of the method is going to be continued on more datasets and it will be compared with other prediction methods in future work.
the authors wish to express their acknowledgments to professor imed kacem, university of lorraine, france, for his constructive comments and suggestions, which helped to improve the quality of this paper.


text:
For your application to be successful, you must be able to show thorough knowledge and experience of paid social ad targeting including audience segmentation and advanced retargeting across a number of social platforms.
Due to the high profile of this role, we are expecting a high number of applicants. Please register and submit your CV ASAP to be considered!

lowercased:
for your application to be successful, you must be able to show thorough knowledge and experience of paid social ad targeting including audience segmentation and advanced retargeting across a number of social platforms.
due to the high profile of this role, we are expecting a high number of applicants. please register and submit your cv asap to be considered!


text: I’d just like to personally vouch for Emma’s chocolate ricotta icebox cake and lemon tart…both are AMAZING! I’ve been lucky enough to snag pieces from each, and I was blown away. Talk about upscale-bakery-quality. If you’re looking for a beautiful and delicious dessert to wow your guests or treat the fam, Emma’s your girl!

lowercased: i’d just like to personally vouch for emma’s chocolate ricotta icebox cake and lemon tart…both are amazing! i’ve been lucky enough to snag pieces from each, and i was blown away. talk about upscale-bakery-quality. if you’re looking for a beautiful and delicious dessert to wow your guests or treat the fam, emma’s your girl!


text:
Do you need a court reporter in Palo Alto, CA?
Court Reporters and Conference Rooms, Palo Alto, CA - MB Reporting has court reporters to cover your deposition needs in Palo Alto and surrounding cities. We also provide interpreting, and video services in Palo Alto, CA, and surrounding cities throughout the Bay Area. If you’d like to schedule a court reporter in Palo Alto, CA, please contact us at (925) 989-6080 or you may email us at depos@mbreporting.com.
We do not have a conference room in Palo Alto. The closest location is Redwood City. If you need a court reporter in Palo Alto, CA, give us a call. Book online now.
Palo Alto Court Reporting Suite Reservations - To schedule a court reporter in Palo Alto, please schedule online or call (925) 989-6080. We can arrange for a court reporter, interpreter and videographer for your deposition. All court reporters are state licensed, prompt and courteous. Our goal is your complete satisfaction that is why we only work with the best court reporters available. Our interpreters are all certified.

lowercased:
do you need a court reporter in palo alto, ca?
court reporters and conference rooms, palo alto, ca - mb reporting has court reporters to cover your deposition needs in palo alto and surrounding cities. we also provide interpreting, and video services in palo alto, ca, and surrounding cities throughout the bay area. if you’d like to schedule a court reporter in palo alto, ca, please contact us at (925) 989-6080 or you may email us at depos@mbreporting.com.
we do not have a conference room in palo alto. the closest location is redwood city. if you need a court reporter in palo alto, ca, give us a call. book online now.
palo alto court reporting suite reservations - to schedule a court reporter in palo alto, please schedule online or call (925) 989-6080. we can arrange for a court reporter, interpreter and videographer for your deposition. all court reporters are state licensed, prompt and courteous. our goal is your complete satisfaction that is why we only work with the best court reporters available. our interpreters are all certified.


text:
woodland areas or in your shade garden.
but they spread indefinately by rhizomes just below the soil surface.
which consist of a cluster of tiny greenish flowers surrounded by four creamy white bracts.
for many species of wild birds and other wildlife.
In the fall, the shiny, deeply veined foliage turns to a beautiful burgundy-red.
Unfortunately, Bunchberry plants are pretty fussy about where they will grow, but they are well worth the effort it takes to help them thrive.
They will tolerate morning sun, but should be grown in partial to full shade for the best results.
Creeping Dogwoods demand a loose, moisture retaining, humus rich soil, so add lots of compost and peat moss to the planting hole.
They will not tolerate alkaline soil at all, requiring a soil pH of 6.8 or lower.
These are very heat sensitive plants that will wither and die if soil temperatures exceed 65°, or if the soil ever dries out completely.
A heavy mulching with peat moss or pine needles will help the soil retain moisture and keep it cooler.
Water regularly and thoroughly during dry periods.
Bunchberries can be propagated by cuttings taken just below the soil surface in mid summer.
Strike in a moist, 50/50 mix of sand and peat moss.
Bunchberry seeds can be planted ¾" deep in the garden in the fall.
The seeds require a period of stratification for germination, so if you are starting them indoors, store them in moistened peat moss in your refrigerator for three month prior to sowing into flats or pots.
Unfortunately, germination can take two or three years.

lowercased:
woodland areas or in your shade garden.
but they spread indefinately by rhizomes just below the soil surface.
which consist of a cluster of tiny greenish flowers surrounded by four creamy white bracts.
for many species of wild birds and other wildlife.
in the fall, the shiny, deeply veined foliage turns to a beautiful burgundy-red.
unfortunately, bunchberry plants are pretty fussy about where they will grow, but they are well worth the effort it takes to help them thrive.
they will tolerate morning sun, but should be grown in partial to full shade for the best results.
creeping dogwoods demand a loose, moisture retaining, humus rich soil, so add lots of compost and peat moss to the planting hole.
they will not tolerate alkaline soil at all, requiring a soil ph of 6.8 or lower.
these are very heat sensitive plants that will wither and die if soil temperatures exceed 65°, or if the soil ever dries out completely.
a heavy mulching with peat moss or pine needles will help the soil retain moisture and keep it cooler.
water regularly and thoroughly during dry periods.
bunchberries can be propagated by cuttings taken just below the soil surface in mid summer.
strike in a moist, 50/50 mix of sand and peat moss.
bunchberry seeds can be planted ¾" deep in the garden in the fall.
the seeds require a period of stratification for germination, so if you are starting them indoors, store them in moistened peat moss in your refrigerator for three month prior to sowing into flats or pots.
unfortunately, germination can take two or three years.


text:
As a solutions provider, we want to make your job in the hospital as easy and flawless as possible. Our brand new, patent-pending RFID solution for patient identification will do just that. RMS Omega’s RestEasy RFID solution uses RFID wristbands, or ankle bands for infants, and an RFID scanner to positively identify patients without disturbing them. RFID scanning does not require a line of site to scan and collect data as with barcode scanning. Since line of site is not necessary when using RFID scanners, nurses will not need to disturb a sleeping patient to scan their wristband or ankle band.
Watch a short video to see how it works!
There’s no need to get close or adjust the infant to scan the ankle band.
Verify infants through blankets and incubators.
No scanner beams, so no damage to infants’ eyes.
Soft and comfortable ankle bands.
In addition to identifying infants, the RestEasy RFID solution can be used to confirm custody of an infant, provide verification for breast milk tracking, medication administration, and specimen tracking.
Using RFID in the NICU has 2 major benefits. First, it promotes sleep in the NICU. Nurses don’t have to unswaddle and move the infant to scan their ankle band since they can scan directly through the incubator and swaddle blankets. So, the infant is left sleeping soundly. Studies have shown that sleep is important for recovery, growth, and development of very young and sick infants. RFID scanning also protects the eyes of an infant. RFID scanners do not have laser beams so there are no strong lights involved with scanning. The infant’s eyes will be left unharmed.
There are 3 main components to the RestEasy RFID solution.
1. UHF RFID reader – it can quickly and easily scan barcodes and RFID tags. There are different versions (Bluetooth enabled, cordless, and corded versions) to best suit the needs of your hospital.
2. RFID ankle bands – RFID thermal ankle bands or wristbands are made with materials and coatings that are specifically engineered for comfort and contact to human skin. They come in a variety of sizes, including infant sizing.
3. RFID printer – Zebra’s ZD500R RFID printer can easily encode and print UHF RFID wristbands and ankle bands for patients of all sizes. It has a small footprint and can fit into your busy workstation.
Watch a short video to see the RestEasy RFID in action.

lowercased:
as a solutions provider, we want to make your job in the hospital as easy and flawless as possible. our brand new, patent-pending rfid solution for patient identification will do just that. rms omega’s resteasy rfid solution uses rfid wristbands, or ankle bands for infants, and an rfid scanner to positively identify patients without disturbing them. rfid scanning does not require a line of site to scan and collect data as with barcode scanning. since line of site is not necessary when using rfid scanners, nurses will not need to disturb a sleeping patient to scan their wristband or ankle band.
watch a short video to see how it works!
there’s no need to get close or adjust the infant to scan the ankle band.
verify infants through blankets and incubators.
no scanner beams, so no damage to infants’ eyes.
soft and comfortable ankle bands.
in addition to identifying infants, the resteasy rfid solution can be used to confirm custody of an infant, provide verification for breast milk tracking, medication administration, and specimen tracking.
using rfid in the nicu has 2 major benefits. first, it promotes sleep in the nicu. nurses don’t have to unswaddle and move the infant to scan their ankle band since they can scan directly through the incubator and swaddle blankets. so, the infant is left sleeping soundly. studies have shown that sleep is important for recovery, growth, and development of very young and sick infants. rfid scanning also protects the eyes of an infant. rfid scanners do not have laser beams so there are no strong lights involved with scanning. the infant’s eyes will be left unharmed.
there are 3 main components to the resteasy rfid solution.
1. uhf rfid reader – it can quickly and easily scan barcodes and rfid tags. there are different versions (bluetooth enabled, cordless, and corded versions) to best suit the needs of your hospital.
2. rfid ankle bands – rfid thermal ankle bands or wristbands are made with materials and coatings that are specifically engineered for comfort and contact to human skin. they come in a variety of sizes, including infant sizing.
3. rfid printer – zebra’s zd500r rfid printer can easily encode and print uhf rfid wristbands and ankle bands for patients of all sizes. it has a small footprint and can fit into your busy workstation.
watch a short video to see the resteasy rfid in action.


text:
On November 7, the Quebec government moved forward with Bill 60, which retains the provision of prohibiting public sector employees from wearing conspicuous religious symbols, dropping the previously proposed exemption for universities and hospitals and substituting a time-limited transition to full implementation.
At its meeting of November 28th, McGill’s Board of Governors passed the same resolution. This resolution is in keeping with previous statements regarding the principles of the McGill community.
As is normal procedure for bills, the Committee on Institutions of the Quebec National Assembly will hold public hearings beginning January 14, 2014. We believe that McGill University should use these hearings to make our views known. To this end, the University will submit a brief to the Committee and request to appear at the hearings.

lowercased:
on november 7, the quebec government moved forward with bill 60, which retains the provision of prohibiting public sector employees from wearing conspicuous religious symbols, dropping the previously proposed exemption for universities and hospitals and substituting a time-limited transition to full implementation.
at its meeting of november 28th, mcgill’s board of governors passed the same resolution. this resolution is in keeping with previous statements regarding the principles of the mcgill community.
as is normal procedure for bills, the committee on institutions of the quebec national assembly will hold public hearings beginning january 14, 2014. we believe that mcgill university should use these hearings to make our views known. to this end, the university will submit a brief to the committee and request to appear at the hearings.


text:
In IntelliJ IDEA HQL console can be used with pure JPA facets (as well as with Hibernate facets) to run JPAQL and HQL queries. All you need is Hibernate libraries and JDBC driver in a module classpath. If persistence.xml contains no Hibernate-specific database connection options, it should be mapped to an existing data source via JPA facet settings.
Tip: HQL console can also be used to generate DDL statements for JPA and Hibernate mappings via its Generate DDL command.
Update: If a Persistence Unit or Session Factory is mapped to a valid local Data Source there is no need to specify JDBC driver jars in a module classpath (Since 8.1.1).
This entry was posted in Tips & Tricks and tagged Code Generation, Database, Hibernate, JPA. Bookmark the permalink.
What if the entitymanagerfactory (JPA) is Spring configured? How to make the HQL console work?
persistence.xml only contains an empty PU def with RESOURCE_LOCAL.
Currently only Hibernate LocalSessionFactoryBean/AnnotationSessionFactoryBean are supported by SpringHibernateIntegration plugin.
Actually you are the first person to ask this question.
As a workaround configure the empty PU to contain all the required mappings, map it to a DataSource with valid JDBC parameters (driver/url/password) and use it to run console as if spring configuration doesn’t exist.
hi, Gregory. all the features look really promising, but there are questions.
you probably know, how to fix it.

lowercased:
in intellij idea hql console can be used with pure jpa facets (as well as with hibernate facets) to run jpaql and hql queries. all you need is hibernate libraries and jdbc driver in a module classpath. if persistence.xml contains no hibernate-specific database connection options, it should be mapped to an existing data source via jpa facet settings.
tip: hql console can also be used to generate ddl statements for jpa and hibernate mappings via its generate ddl command.
update: if a persistence unit or session factory is mapped to a valid local data source there is no need to specify jdbc driver jars in a module classpath (since 8.1.1).
this entry was posted in tips & tricks and tagged code generation, database, hibernate, jpa. bookmark the permalink.
what if the entitymanagerfactory (jpa) is spring configured? how to make the hql console work?
persistence.xml only contains an empty pu def with resource_local.
currently only hibernate localsessionfactorybean/annotationsessionfactorybean are supported by springhibernateintegration plugin.
actually you are the first person to ask this question.
as a workaround configure the empty pu to contain all the required mappings, map it to a datasource with valid jdbc parameters (driver/url/password) and use it to run console as if spring configuration doesn’t exist.
hi, gregory. all the features look really promising, but there are questions.
you probably know, how to fix it.