Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
32nd (2018)
Session ID : 2J2-03
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Classification by Time-Series Gradient Boosting Tree
Application to Financial Time-Series Prediciton
*Kei NAKAGAWAMitsuyoshi IMAMURAKenichi YOSHIDA
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CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

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.

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© 2018 The Japanese Society for Artificial Intelligence
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