20. The system of claim 19 , the forecaster being operative to predict future observations relative to a predictor variable as a function of a posterior distribution of at least one leaf of the regression model.
19. A system that facilitates forecasting of time series data, comprising: a computer processor that stores, in a memory communicatively coupled to the computer processor, computer-executable instructions, the execution of which by the processor makes predictions relative to time series data and performs data mining of large amounts of data, the predictions related to nontrivial extractions of implicit, previously unknown information obtained by the data mining from the large amounts of data; a model generator that generates a decision graph employed for predicting future observations of time series data, the decision graph is structured as a regression model via a greedy search algorithm comprising: at least one non-leaf node associated with a Boolean function for one of a plurality of previous variables for the time series; a plurality of leaves, each of the plurality of leaves being associated with at least one functional formula corresponding to a non-trivial linear regression for previous observations in the time series data; respective edges associating respective the functional formulas of the plurality of leaves with parent nodes according to the Boolean functions along a path that includes each of the at least one non-leaf node that is a parent relative to the respective plurality of leaves; at least one non-root node of the decision graph having more than one parent node; a forecaster that employs the regression model obtained from the model generator to forecast one or more future observations of the time series data wherein the forecaster iterates through a set of potential regressors ordered in a descending manner of their correlation for a predicted variable associated with the one or more future observations, therefore terminating the greedy search algorithm when addition of a given regressor does not improve model score and performing a merge operation to the leaves after the model has more than two leaves 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; and a display comprising output of the one or more future observations of the time series data, including predictions relative to time series data, the predictions related to nontrivial extractions of implicit, previously unknown information obtained by the data mining within the large amounts of data.