Time-series gradient boosting tree for stock price prediction

Kei Nakagawa and Kenichi Yoshida

International Journal of Data Mining, Modelling and Management, 2022, vol. 14, issue 2, 110-125

Abstract: We propose a time-series gradient boosting tree for a dataset with time-series and cross-sectional attributes. Our time-series gradient boosting tree has weak learners with time-series and cross-sectional attributes in its internal node, and split examples based on similarity between a pair of time-series or impurity between cross-sectional attributes. Dissimilarity between a pair of time-series is defined by the dynamic time warping method. In other words, the decision tree is constructed based on the shape that the time-series is similar or not similar to its past shape. We conducted an empirical analysis using major world indices and confirmed that our time-series gradient boosting tree is superior to prior research methods in terms of both profitability and accuracy.

Keywords: dynamic time warping method; time-series decision tree; time-series gradient boosting tree; stock price prediction. (search for similar items in EconPapers)
Date: 2022
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