Time-series gradient boosting tree for stock price prediction
by Kei Nakagawa; Kenichi Yoshida
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 14, No. 2, 2022

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.

Online publication date: Sat, 11-Jun-2022

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