1. A computer-implemented data analysis method that makes predictions relative to time series data, the predictions related to nontrivial extractions of implicit, previously unknown information obtained by data mining within large amounts of data, the method comprising: storing, in a memory communicatively coupled to a processor, computer-executable instructions for performing the method of making predictions relative to time series data, the predictions related to nontrivial extractions of implicit, previously unknown information obtained by data mining within large amounts of data; executing the instructions on the processor, wherein the instructions result in data mining within the large amounts of data; according to the instructions being executed: employing a Bayesian model selection approach to construct a decision graph based on the data relating to observations of time series data, the decision graph having a model structure that includes at least two leaves, at least one leaf of the decision graph including at least one nontrivial linear regression wherein the Bayesian model selection approach comprising a greedy search algorithm to grow the model by adding leaves to a model so long as the model improves and performing a merge operation to the leaves after the model has more than two leaves; providing a set of potential regressors having variables associated with the data, wherein the potential regressors are ordered in a descending order according to their correlation relative to a target variable to be predicted, the greedy search algorithm being performed iteratively relative to respective leaves of the model for a subset of potential regressors and, wherein the non-trivial linear regression at the at least one leaf corresponding to at least one variable of the set of potential regressors and the merge operator operates on at least two leaves so that at least one non-root node of the decision graph has more than one parent node; computing a Bayesian score for a split leaf model and a merge model; storing the model with the higher score computed Bayesian score; repeating the performance of the greedy search and computation of the Bayesian score so long as the model score improves; terminating the iterative process if a regressor does not improve the model score; employing the decision graph to predict future observations in the time series data; employing a split leaf operator at one leaf of the decision graph to grow the decision graph to include additional leaves, each of the additional leaves including at least one linear regression on at least one variable of the set of potential regressors; storing or displaying the predicted future observations of the time series data, wherein the predicted future observations are based on the decision graph and include implicit, previously unknown information obtained from mining the data.
1. A computer-implemented data analysis method that makes predictions relative to time series data, the predictions related to nontrivial extractions of implicit, previously unknown information obtained by data mining within large amounts of data, the method comprising: storing, in a memory communicatively coupled to a processor, computer-executable instructions for performing the method of making predictions relative to time series data, the predictions related to nontrivial extractions of implicit, previously unknown information obtained by data mining within large amounts of data; executing the instructions on the processor, wherein the instructions result in data mining within the large amounts of data; according to the instructions being executed: employing a Bayesian model selection approach to construct a decision graph based on the data relating to observations of time series data, the decision graph having a model structure that includes at least two leaves, at least one leaf of the decision graph including at least one nontrivial linear regression wherein the Bayesian model selection approach comprising a greedy search algorithm to grow the model by adding leaves to a model so long as the model improves and performing a merge operation to the leaves after the model has more than two leaves; providing a set of potential regressors having variables associated with the data, wherein the potential regressors are ordered in a descending order according to their correlation relative to a target variable to be predicted, the greedy search algorithm being performed iteratively relative to respective leaves of the model for a subset of potential regressors and, wherein the non-trivial linear regression at the at least one leaf corresponding to at least one variable of the set of potential regressors and the merge operator operates on at least two leaves so that at least one non-root node of the decision graph has more than one parent node; computing a Bayesian score for a split leaf model and a merge model; storing the model with the higher score computed Bayesian score; repeating the performance of the greedy search and computation of the Bayesian score so long as the model score improves; terminating the iterative process if a regressor does not improve the model score; employing the decision graph to predict future observations in the time series data; employing a split leaf operator at one leaf of the decision graph to grow the decision graph to include additional leaves, each of the additional leaves including at least one linear regression on at least one variable of the set of potential regressors; storing or displaying the predicted future observations of the time series data, wherein the predicted future observations are based on the decision graph and include implicit, previously unknown information obtained from mining the data. 7. The method of claim 1 , further comprising scoring the model after growing the model via the greedy search algorithm to provide a model score, and comparing the model score of a current model relative to a previous model to discern whether the model is improving.