Methods and systems are disclosed for learning a regression decision graph
model using a Bayesian model selection approach. In a disclosed aspect,
the model structure and/or model parameters can be learned using a greedy
search algorithm applied to grow the model so long as the model improves.
This approach enables construction of a decision graph having a model
structure that includes a plurality of leaves, at least one of which
includes a non-trivial linear regression. The resulting model thus can be
employed for forecasting, such as for time series data, which can include
single or multi-step forecasting.