Host: The Japanese Society for Artificial Intelligence
Name : The 32nd Annual Conference of the Japanese Society for Artificial Intelligence, 2018
Number : 32
Location : [in Japanese]
Date : June 05, 2018 - June 08, 2018
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.