Tennis Match Trend Prediction Based on LSTM
DOI:
https://doi.org/10.54097/tbbaaz14Keywords:
Data Mining, Random Forest, LSTM, Attention Mechanism, Sensitivity Analysis.Abstract
The focus of this research is on the data processing, including data mining, data cleaning, feature extraction, feature engineering and other aspects. Whether the data can be used to fit the game trend well is the key to the final effect of the model. At the same time, there are many reasons (i.e., characteristics) that affect the trend of the game. The purpose of this study is to find out the most drastic variables and make corresponding tactical changes according to them. The model based on LSTM network constructed in this paper has a good ability to handle variable-length inputs, and uses the attention mechanism to enhance the "memory" of long-distance inputs, and uses the Dropout layer to avoid model overfitting to a certain extent. Finally, the random forest method was used to extract several variables with the highest sensitivity for the sensitivity analysis of the model.
Downloads
References
Li JY, Liu CL, Gong YF. Layer Trajectory LSTM[j]. 19TH Annual conference of The International Speech Communication Association (INTERSPEECH 2018), 2018, 1-6:1768-1772.
Li Y, Lu YF. LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes[j]. 7th International Conference on Advanced Cloud and Big Data (CBD), 2019, 180-189.
M. S. Alshehri, J. Ahmad, S. Almakdi, M. A. Qathrady, Y. Y. Ghadi and W. J. Buchanan, SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT[j], IEEE Access, 2024, 12:35521-35538.
Aziz Abdulla P, Ben Henda N, Mayr R. Decisive Markov Chains [J]. arXiv e-prints, 2007: arXiv: 0706.2585.
Liang YX, Wu JY, Wang, W, Cao, YJ, Zhong BL, Chen ZK, Li ZZ. Product Marketing Prediction based on XGboost and LightGBM Algorithm [J]. 2019 2ND International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2019), 2019, 150-153.
Utkin LV, Kovalev MS, Meldo AA, Coolen FPA. Imprecise Extensions of Random Forests and Random Survival Forests [J]. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA 2019), 2019, 103:404-413.
Hu, Xiaojian, Liu, Tong, Hao, Xiatong, Lin, Chenxi. Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction [J]. Journal of Supercomputing, 2022, 78 (10):12686-12709.
Guo J, Qi L, Shi Y, et al. PLACE Dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization [J]. ACM Transactions on Multimedia Computing, Communications and Applications, 2023, 20(3): 1-23.
Xie Y, Liang RY, Liang ZL, Zhao L. Attention-Based Dense LSTM for Speech Emotion Recognition [J]. IEICE Transactions on Information and Systems, 2019, E102D (7):1426-1429.
Bhattacharya S, Maddikunta PKR, Meenakshisundaram I, Gadekallu TR, Sharma S, Alkahtani M, Abidi MH. Deep Neural Networks Based Approach for Battery Life Prediction [J]. CMC-Computers Materials & Continua, 2021, 69(2):2599-2615.
Sari WK, Rini DP, Malik RF. Text Classification Using Long Short-Term Memory [J].2019 3RD International Conference on Electrical Engineering and Computer Science (ICECOS 2019),2019,150-155.
Yang HF, Hu JJ, Cai JH, Wang YP, Chen X, Zhao X, Wang LL. A New MC-LSTM Network Structure Designed for Regression Prediction of Time Series [J]. Neural Processing Letters, 2023,55(7):8957-8979.
Zeng Z, Jiang Z, Men A, et al. A plug-and-play LSTM-based attention module for person re-identification[C]//Eleventh International Conference on Graphics and Image Processing (ICGIP 2019). SPIE, 2020, 11373: 83-89.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
