TCDformer-based Momentum Transfer Model for Long-term Sports Prediction
This work addresses the need for accurate long-term predictions in sports, such as for professional coaches to develop training strategies, but it appears incremental as it builds on existing methods like TCDformer and momentum encoding.
The paper tackles the problem of long-term sports prediction, which is challenging due to variable distributions and multi-level matches, by proposing TM2, a TCDformer-based Momentum Transfer Model, resulting in a 61.64% reduction in MSE and 63.64% reduction in MAE on the 2023 Wimbledon men's tournament datasets.
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%.