[2J2] [General Session] 13. AI Application

[2J2-03] Classification by Time-Series Gradient Boosting Tree

Application to Financial Time-Series Prediciton

〇Kei Nakagawa^1,2, Mitsuyoshi Imamura^1,3, Kenichi Yoshida² (1. Nomura Asset Management Co.,Ltd., 2. University of Tsukuba Graduate School of Business Sciences, 3. University of Tsukuba Graduate School of Systems and Information Engineering)

Keywords:Time-Series Decision Tree, Gradient Boosting Tree, Time-Series Gradient Boosting Tree, Stock Price Prediction

We propose a time-series gradient boosting decision tree for a data set with time-series and cross-sectional attributes.
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 cross-sectional attributes.
Dissimilarity between a pair of time-series is defined by dynamic time warping method or in financial time-seires by indexing dynamic time warping method.
Experimental results with stock price prediction confirm that our method constructs interpretable and accurate decision trees.