Momentum-Based Race Analysis and GRU-Based Predictive Modeling
DOI:
https://doi.org/10.62051/ijcsit.v7n2.01Keywords:
Momentum, Correlation test, Machine learning, Binary logistic regression, GRU, Confusion matrixAbstract
This study analyzes the data from the 2023 Wimbledon Men's Tennis Final, defines the concept of "momentum", and constructs a time - series prediction model based on the Gated Recurrent Unit (GRU). The objective is to develop a model capable of precisely capturing the point - by - point dynamic states during the match and presenting them in a visual format. Additionally, it validates the stochastic characteristics of the "momentum" phenomenon and evaluates its influence mechanism on the match trends.
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References
[1] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. KDD.
[2] Ke, G., Meng, Q., et al. (2017). LightGBM: A highly efficient gradient boosting decision tree. NeurIPS.
[3] Cho, K., et al. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. EMNLP.
[4] Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
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