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We propose a tree ensemble method, referred to as time series forest (TSF),for time series classification. TSF employs a combination of the entropy gainand a distance measure, referred to as the Entrance (entropy and distance)gain, for evaluating the splits. Experimental studies show that the Entrancegain criterion improves the accuracy of TSF. TSF randomly samples features ateach tree node and has a computational complexity linear in the length of atime series and can be built using parallel computing techniques such asmulti-core computing used here. The temporal importance curve is also proposedto capture the important temporal characteristics useful for classification.Experimental studies show that TSF using simple features such as mean,deviation and slope outperforms strong competitors such as one-nearest-neighborclassifiers with dynamic time warping, is computationally efficient, and canprovide insights into the temporal characteristics.

A Time Series Forest for Classification and Feature Extraction