Prediction of Time Series Data based on PCA-HMM-BN

This present work describes a framework for the analysis of score dynamics and energy fluctuations in competitive environments, aimed at forecasting match outcomes and providing strategic decisions useful to coaching and players’ performance. Potential energy was defined, seven key measures were identified related to Kendall’s tau, shown as heat maps to outline the underlying patterns. Using the CV method and the AHP composite weighting, overall scores can determine the overall possible energy level of players. The line graph should be drawn based on the latter in order to assess volatility throughout the course of the match. Then, it carries out a derivation method for potential energy series, followed by extracting the most critical turning points in energy dynamics through the PCA-HMM-BN algorithm. Feature engineering with lag features and effect coding improves predictability. This graph presents a comparison among different machine learning models, namely BP Neural Network, XGBoost, and SVM, by showing the high performance obtained by an SVM model tuned with the ASAA algorithm, with an R-squared score of 0.99967.