Summary of Tcdformer-based Momentum Transfer Model For Long-term Sports Prediction, by Hui Liu et al.
TCDformer-based Momentum Transfer Model for Long-term Sports Prediction
by Hui Liu, Jiacheng Gu, Xiyuan Huang, Junjie Shi, Tongtong Feng, Ning He
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes TM2, a novel approach for long-term sports prediction that leverages momentum transfer to enhance accuracy. Traditional methods often rely on complex statistical techniques, but this can be limited by dataset scale and struggle with variable distributions. The TM2 model comprises a momentum encoding module based on local linear scaling approximation (LLSA) and a prediction module that decomposes time series into trend and seasonal components using multilayer perceptron (MLP) and wavelet attention mechanisms. Experimental results show that TM2 outperforms existing models, reducing mean squared error (MSE) by 61.64% and mean absolute error (MAE) by 63.64% on the 2023 Wimbledon men’s tournament dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps coaches make better predictions about sports games. Right now, they use complicated math to try to guess what will happen in a game. But this isn’t very good at predicting things that happen over a long time or when there are many variables involved. The new model, called TM2, tries to fix this problem by using momentum transfer. This means it looks at how the game has been going so far and uses that information to make better predictions about what will happen next. The results show that TM2 is much better than other models at predicting sports games, especially when looking ahead a long time. |
Keywords
» Artificial intelligence » Attention » Mae » Mse » Time series
