[3Pin1] インタラクティブ(1)

[3Pin1-12] Experimental evaluation of Time-Series Gradient Boosting Tree with Time-Series Benchmark datasets

〇Mitsuyoshi Imamura^1,2, Kei Nakagawa^1,2, Kenichi Yoshida¹ (1. University of Tsukuba, 2. Nomura Asset Management Ltd.)

Keywords:Time-Series Tree, Gradient Boosting Tree, Time-Series Gradient Boosting Tree

In this paper, We evaluated the time-series gradient boosting decision tree method using benchmark data.
Our time-series gradient boosting tree has weak learners with time-series and cross-sectional attribute in its internal node, and split examples based on dissimilarity between a pair of time-series or impurity between a pair of cross-sectional attributes.It has been empirically observed that the method induces accurate and comprehensive decision trees in time-series classification, which has gaining increasing attention due to its importance in various real-world applications.