TCDformer-based Momentum Transfer Model for Long-term Sports Prediction

TL;DR

This paper introduces TM2, a novel TCDformer-based model that effectively predicts long-term sports outcomes by encoding momentum and decomposing time series into trend and seasonal components, outperforming existing models.

Contribution

The paper presents a new momentum transfer model using TCDformer architecture for long-term sports prediction, addressing dataset scale and distribution challenges.

Findings

Reduces MSE by 61.64% on Wimbledon data

Reduces MAE by 63.64% on Wimbledon data

Outperforms existing sports prediction models

Abstract

Accurate sports prediction is a crucial skill for professional coaches, which can assist in developing effective training strategies and scientific competition tactics. Traditional methods often use complex mathematical statistical techniques to boost predictability, but this often is limited by dataset scale and has difficulty handling long-term predictions with variable distributions, notably underperforming when predicting point-set-game multi-level matches. To deal with this challenge, this paper proposes TM2, a TCDformer-based Momentum Transfer Model for long-term sports prediction, which encompasses a momentum encoding module and a prediction module based on momentum transfer. TM2 initially encodes momentum in large-scale unstructured time series using the local linear scaling approximation (LLSA) module. Then it decomposes the reconstructed time series with momentum transfer into trend and seasonal components. The final prediction results are derived from the additive combination of a multilayer perceptron (MLP) for predicting trend components and wavelet attention mechanisms for seasonal components. Comprehensive experimental results show that on the 2023 Wimbledon men's tournament datasets, TM2 significantly surpasses existing sports prediction models in terms of performance, reducing MSE by 61.64% and MAE by 63.64%.