Exploring Patterns Behind Sports

TL;DR

This paper introduces a hybrid ARIMA-LSTM framework with feature engineering for accurate sports time series prediction, combining statistical and deep learning methods to capture complex patterns and improve forecasting performance.

Contribution

The paper presents a novel hybrid model integrating ARIMA and LSTM with feature engineering techniques like embedding and PCA for enhanced sports time series forecasting.

Findings

The hybrid model achieves low RMSE and MAE scores, indicating high accuracy.

Ablation studies confirm the importance of each component in the model.

SHAP analysis reveals key features influencing predictions.

Abstract

This paper presents a comprehensive framework for time series prediction using a hybrid model that combines ARIMA and LSTM. The model incorporates feature engineering techniques, including embedding and PCA, to transform raw data into a lower-dimensional representation while retaining key information. The embedding technique is used to convert categorical data into continuous vectors, facilitating the capture of complex relationships. PCA is applied to reduce dimensionality and extract principal components, enhancing model performance and computational efficiency. To handle both linear and nonlinear patterns in the data, the ARIMA model captures linear trends, while the LSTM model models complex nonlinear dependencies. The hybrid model is trained on historical data and achieves high accuracy, as demonstrated by low RMSE and MAE scores. Additionally, the paper employs the run test to assess the randomness of sequences, providing insights into the underlying patterns. Ablation studies are conducted to validate the roles of different components in the model, demonstrating the significance of each module. The paper also utilizes the SHAP method to quantify the impact of traditional advantages on the predicted results, offering a detailed understanding of feature importance. The KNN method is used to determine the optimal prediction interval, further enhancing the model's accuracy. The results highlight the effectiveness of combining traditional statistical methods with modern deep learning techniques for robust time series forecasting in Sports.