Time-series gradient boosting tree for stock price prediction Online publication date: Sat, 11-Jun-2022
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
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com