{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:30:54Z","timestamp":1763343054529,"version":"3.45.0"},"reference-count":53,"publisher":"Tech Science Press","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.065413","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T04:35:06Z","timestamp":1745555706000},"page":"5393-5412","source":"Crossref","is-referenced-by-count":0,"title":["Pitcher Performance Prediction Major League Baseball (MLB) by Temporal Fusion Transformer"],"prefix":"10.32604","volume":"83","author":[{"given":"Wonbyung","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jang Hyun","family":"Kim","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","series-title":"The 15th Annual MIT Sloan Sports Analytics Conference","first-page":"1","article-title":"Baseball predictions and strategies using explainable AI","author":"Silver","year":"2021 Apr 8\u20139"},{"key":"ref2","first-page":"497","article-title":"High-school baseball pitcher\u2019s ERA (Earned Run Average) prediction using multi-variable linear regression analysis method","volume":"14","author":"Oh","year":"2020","journal-title":"J Knowl Inf Technol Syst"},{"key":"ref3","first-page":"7","article-title":"Sabermetric analysis: wins-above-replacement","volume":"3","author":"Hendela","year":"2020","journal-title":"Locus: Seton Hall J Undergrad Res"},{"key":"ref4","unstructured":"Moorefield J. The Oakland Athletics use of sabermetrics and the rise of big data analytics in business [master\u2019s thesis]. Chattanooga, TN, USA: University of Tennessee at Chattanooga; 2021."},{"key":"ref5","series-title":"2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)","first-page":"1597","article-title":"Gate-variants of gated recurrent unit (GRU) neural networks","author":"Dey"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-3-642-24797-2_4","author":"Graves","year":"2012","journal-title":"Long short-term memory. Supervised sequence labelling with recurrent neural networks"},{"key":"ref7","first-page":"1","article-title":"Bi-LSTM-based two-stream network for machine remaining useful life prediction","volume":"71","author":"Jin","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur J Oper Res"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"Int J Forecast"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"14379","DOI":"10.1007\/s00500-023-09091-y","article-title":"Designing a prediction model for athlete\u2019s sports performance using neural network","volume":"27","author":"Zhao","year":"2023","journal-title":"Soft Computing"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1080\/17461391.2018.1480662","article-title":"Selection procedures in sports: improving predictions of athletes\u2019 future performance","volume":"18","author":"Den Hartigh","year":"2018","journal-title":"Eur J Sport Sci"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.knosys.2008.03.016","article-title":"A compound framework for sports prediction: the case study of football","volume":"21","author":"Min","year":"2008","journal-title":"Knowl Based Syst"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"3916383","DOI":"10.1155\/2022\/3916383","article-title":"Sports performance prediction based on chaos theory and machine learning","volume":"2022","author":"Sun","year":"2022","journal-title":"Wirel Commun Mob Comput"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"7238789","DOI":"10.1155\/2022\/7238789","article-title":"Prediction model and data simulation of sports performance based on the artificial intelligence algorithm","volume":"2022","author":"Lu","year":"2022","journal-title":"Comput Intell Neurosci"},{"key":"ref15","series-title":"International Conference on Advanced Information Networking and Applications","first-page":"668","article-title":"Sports data mining for cricket match prediction","author":"Anuraj"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1055\/a-1993-2371","article-title":"Prediction of marathon performance using artificial intelligence","volume":"44","author":"Lerebourg","year":"2023","journal-title":"Int J Sports Med"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"4499","DOI":"10.3390\/app11104499","article-title":"Use of machine learning and deep learning to predict the outcomes of major league baseball matches","volume":"11","author":"Huang","year":"2021","journal-title":"Appl Sci"},{"key":"ref18","unstructured":"Cui AY. Forecasting outcomes of major league baseball games using machine learning [master\u2019s thesis]. Philadelphia, PA, USA: University of Pennsylvania; 2020."},{"key":"ref19","doi-asserted-by":"crossref","first-page":"288","DOI":"10.3390\/e24020288","article-title":"Exploring and selecting features to predict the next outcomes of MLB games","volume":"24","author":"Li","year":"2022","journal-title":"Entropy"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s41060-022-00313-4","article-title":"Performance prediction in major league baseball by long short-term memory networks","volume":"15","author":"Sun","year":"2023","journal-title":"Int J Data Sci Anal"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/00031305.2017.1401959","article-title":"Predicting home run production in Major League Baseball using a Bayesian semiparametric model","volume":"72","author":"Fellingham","year":"2018","journal-title":"Am Stat"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"84","DOI":"10.3390\/stats3020008","article-title":"The prediction of batting averages in major league baseball","volume":"3","author":"Bailey","year":"2020","journal-title":"Stats"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1080\/08839514.2018.1442991","article-title":"Machine learning applications in baseball: a systematic literature review","volume":"31","author":"Koseler","year":"2017","journal-title":"Appl Artif Intell"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCG.2016.101","article-title":"Statcast dashboard: exploration of spatiotemporal baseball data","volume":"36","author":"Lage","year":"2016","journal-title":"IEEE Comput Graph Appl"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1119\/1.4976652","article-title":"Statcast and the baseball trajectory calculator","volume":"55","author":"Kagan","year":"2017","journal-title":"Phys Teach"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/aris.2010.1440440110","article-title":"Sports knowledge management and data mining","volume":"44","author":"Schumaker","year":"2010","journal-title":"Annu Rev Inf Sci Technol"},{"key":"ref27","first-page":"7","article-title":"A review of data mining techniques for result prediction in sports","volume":"2","author":"Rastegari","year":"2013","journal-title":"Adv Comput Sci"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/10413200305400","article-title":"Sport-specific practice and the development of expert decision-making in team ball sports","volume":"15","author":"Baker","year":"2003","journal-title":"J Appl Sport Psychol"},{"key":"ref29","first-page":"110","article-title":"Performance analysis for decision making in team sports","volume":"13","author":"Lorains","year":"2013","journal-title":"Int J Perf Anal Sport"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"54","DOI":"10.53841\/bpssepr.2016.12.1.54","article-title":"Enhancing decision-making during sports performance: current understanding and future directions","volume":"12","author":"Cotterill","year":"2016","journal-title":"Sport Exerc Psychol Rev"},{"key":"ref31","unstructured":"Mittal H, Rikhari D, Kumar J, Singh AK. A study on machine learning approaches for player performance and match results prediction. arXiv:210810125. 2021."},{"key":"ref32","doi-asserted-by":"crossref","first-page":"5510","DOI":"10.1016\/j.eswa.2008.06.088","article-title":"Prediction of athletes performance using neural networks: an application in cricket team selection","volume":"36","author":"Iyer","year":"2009","journal-title":"Expert Syst Appl"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"5269","DOI":"10.1007\/s12652-021-03497-y","article-title":"Depression prediction based on BiAttention-GRU","volume":"13","author":"Cao","year":"2022","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"76690","DOI":"10.1109\/ACCESS.2019.2921578","article-title":"Air pollution forecasting using a deep learning model based on 1D convnets and bidirectional GRU","volume":"7","author":"Tao","year":"2019","journal-title":"IEEE Access"},{"key":"ref37","series-title":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","first-page":"1","article-title":"Attention-based bidirectional LSTM-CNN model for remaining useful life estimation","author":"Song"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.ymssp.2019.05.005","article-title":"Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme","volume":"129","author":"Yu","year":"2019","journal-title":"Mech Syst Signal Process"},{"key":"ref39","series-title":"2021 9th International Conference on Cyber and IT Service Management (CITSM)","first-page":"1","article-title":"Comparing bitcoin\u2019s prediction model using GRU, RNN, and LSTM by hyperparameter optimization grid search and random search","author":"Buslim"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"100736","DOI":"10.1109\/ACCESS.2021.3097141","article-title":"Intelligent traffic flow prediction using optimized GRU model","volume":"9","author":"Hussain","year":"2021","journal-title":"IEEE Access"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"123990","DOI":"10.1016\/j.energy.2022.123990","article-title":"Interpretable wind speed prediction with multivariate time series and temporal fusion transformers","volume":"252","author":"Wu","year":"2022","journal-title":"Energy"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.neucom.2022.05.083","article-title":"A temporal fusion transformer for short-term freeway traffic speed multistep prediction","volume":"500","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref43","series-title":"2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)","first-page":"60","article-title":"Stock price prediction based on temporal fusion transformer","author":"Hu"},{"key":"ref44","series-title":"Conference on Lifelong Learning Agents","first-page":"586","article-title":"MultiMix TFT: a multi-task mixed-frequency framework with temporal fusion transformers","author":"Deforce"},{"key":"ref45","unstructured":"Lundberg S. A unified approach to interpreting model predictions. arXiv:170507874. 2017."},{"key":"ref46","doi-asserted-by":"crossref","DOI":"10.58248\/PN633","author":"Christoph","year":"2020","journal-title":"Interpretable machine learning: a guide for making black box models explainable"},{"key":"ref47","unstructured":"Lundberg SM, Erion GG, Lee SI. Consistent individualized feature attribution for tree ensembles. arXiv:180203888. 2018."},{"key":"ref48","doi-asserted-by":"crossref","first-page":"8299","DOI":"10.1007\/s10639-022-11536-0","article-title":"Recent advances in predictive learning analytics: a decade systematic review (2012\u20132022)","volume":"28","author":"Sghir","year":"2023","journal-title":"Educ Inf Technol"},{"key":"ref49","unstructured":"Kamarthi H, Rodr\u00edguez A, Prakash BA. Back2Future: leveraging backfill dynamics for improving real-time predictions in future. arXiv:210604420. 2021."},{"key":"ref50","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1037\/0033-295X.113.2.201","article-title":"Short-term memory for serial order: a recurrent neural network model","volume":"113","author":"Botvinick","year":"2006","journal-title":"Psychol Rev"},{"key":"ref51","doi-asserted-by":"crossref","unstructured":"Vari\u0161 D, Bojar O. Sequence length is a domain: length-based overfitting in transformer models. arXiv:210907276. 2021.","DOI":"10.18653\/v1\/2021.emnlp-main.650"},{"key":"ref52","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","author":"Zhou"},{"key":"ref53","unstructured":"Zhang X, Ghosh S. PaEBack: pareto-efficient backsubsampling for time series data. arXiv:221015780. 2022."}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-3\/TSP_CMC_65413\/TSP_CMC_65413.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:28:46Z","timestamp":1763342926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n3\/61073"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.065413","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}