Momentum prediction method based on CUSUM and LightGBM-SHAP model

Zhaofeng Xiong; Yikun Zhang; Huimin Zhu

doi:10.1117/12.3037887

27 September 2024 Momentum prediction method based on CUSUM and LightGBM-SHAP model

Zhaofeng Xiong, Yikun Zhang, Huimin Zhu

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132751I (2024) https://doi.org/10.1117/12.3037887
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

This paper introduces a novel approach for predicting momentum shifts in time-series data by integrating an enhanced Cumulative Sum (CUSUM) algorithm with a Light Gradient Boosting Machine (LightGBM) model, augmented with SHAP values. Our methodology refines CUSUM through a composite sliding window to improve detection accuracy and utilizes LightGBM for efficient prediction, with SHAP values providing interpretability of model predictions. Tested on a dataset of 1701 tennis matches from the 2023 Wimbledon Championships, our method demonstrated superior performance, achieving near-perfect prediction accuracy with an R2 score of 0.999, and significantly lower prediction errors (MAPE and RMSE) than traditional models. These results underline the method's effectiveness in not only accurately predicting momentum shifts but also offering detailed insights into the driving factors behind these changes. This advancement opens new avenues for time-series analysis across various domains, extending well beyond the initial sports analytics application.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhaofeng Xiong, Yikun Zhang, and Huimin Zhu "Momentum prediction method based on CUSUM and LightGBM-SHAP model", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132751I (27 September 2024); https://doi.org/10.1117/12.3037887

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