用动态趋势建模提升体育赛事长期预测准确率
TCDformer-based Momentum Transfer Model for Long-term Sports Prediction
- 基于TCDformer构建动量传递模型，分解时间序列趋势与周期成分
- 在温网2023数据上，MSE降低61.64%，MAE降低63.64%
- 适合需要长周期赛事预测的教练与战术分析团队
精准的体育预测对专业教练制定科学训练与比赛策略至关重要。传统方法依赖复杂统计模型，受限于数据规模，难以处理长期预测及分布变化，尤其在多层级比分赛中表现不佳。为此，本文提出TM2：一种基于TCDformer的动量传递模型，包含动量编码模块与预测模块。该模型首先通过局部线性缩放近似（LLSA）模块对大规模非结构化时间序列进行动量编码；随后利用动量传递机制将重构序列分解为趋势与季节成分；最终由多层感知机（MLP）预测趋势项，结合小波注意力机制处理季节项，实现加法融合预测。大量实验表明，在2023年温布尔登男单数据集上，TM2显著优于现有模型，均方误差（MSE）降低61.64%，平均绝对误差（MAE）降低63.64%。
原文摘要 · Abstract (English)
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%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。