  We propose a tree ensemble method, referred to as time series forest (TSF),
for time series classification. TSF employs a combination of the entropy gain
and a distance measure, referred to as the Entrance (entropy and distance)
gain, for evaluating the splits. Experimental studies show that the Entrance
gain criterion improves the accuracy of TSF. TSF randomly samples features at
each tree node and has a computational complexity linear in the length of a
time series and can be built using parallel computing techniques such as
multi-core computing used here. The temporal importance curve is also proposed
to 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-neighbor
classifiers with dynamic time warping, is computationally efficient, and can
provide insights into the temporal characteristics.
