Hierarchical Estimator

In this paper, a new machine learning solution for function approximation is presented. It combines many simple and relatively inaccurate estimators to achieve high accuracy. It creates – in incremental manner – hierarchical, tree-like structure, adapting it to the specific problem being solved. As most variants use the errors of already constructed parts to direct further construction, it may be viewed as example of boosting – as understood in general sense. The influence of particular constituent estimator on the whole solution’s output is not constant, but depends on the feature vector being evaluated.Provided in this paper are: general form of the metaalgorithm, a few specific, detailed solutions, theoretical basis and experimental results with one week power load prediction for country-wide power distribution grid and on simple test datasets.

► A new method for combining many inaccurate estimators into one, more accurate.
► Designed for nonlinear, continuous, real number or vector output.
► Metaalgorithm with different variants of details possible, examples included.
► Tests included load prediction task for country-wide power distribution grid.
► 1.88% MAPE on test set for that task, no external knowledge embedded.