Multivariate time series forecasting model based on sliding window machine learning

This paper introduces a multivariate time series forecasting model that combines a sliding window technique with machine learning, incorporating a convolutional neural network to extract spatio-temporal features. This integration boosts predictive accuracy and robustness. Applied to the Jilin credit card installment dataset, our SHRF method improves AUC PRECISION and F1 scores. Validations on the BeijingAirQuality dataset using the SHL model show our model reduces RMSE per time step to 15.18, demonstrating excellent performance in multivariable time series forecasting.