论文标题
部分可观测时空混沌系统的无模型预测
Missing data patterns in runners' careers: do they matter?
论文作者
论文摘要
预测年轻跑步者的未来表现是实验运动科学和绩效分析中的重要研究问题。我们在14年期间分析了男性中距离跑步者年度最佳季节性表现的数据集，并提供了一个建模框架，该框架既说明了每个跑步者通常都在三个距离事件（800、1500和5000米）中运行的事实以及没有跑步活动的时期。我们提出了一个潜在的类矩阵变化状态空间模型，我们从经验上证明，考虑跑步者职业中缺少的数据模式可以改善其表现的样本预测随着时间的推移。特别是，我们证明，对于此分析，缺少的数据模式为预测跑步者的性能提供了宝贵的信息。
Predicting the future performance of young runners is an important research issue in experimental sports science and performance analysis. We analyse a data set with annual seasonal best performances of male middle distance runners for a period of 14 years and provide a modelling framework that accounts for both the fact that each runner has typically run in three distance events (800, 1500 and 5000 meters) and the presence of periods of no running activities. We propose a latent class matrix-variate state space model and we empirically demonstrate that accounting for missing data patterns in runners' careers improves the out of sample prediction of their performances over time. In particular, we demonstrate that for this analysis, the missing data patterns provide valuable information for the prediction of runner's performance.
