MPDDF: Multi perspective dual data flow model for time series forecasting

Abstract

Abstract Multivariate time series forecasting tasks are widely used in social production and daily life, and making accurate predictions for the future can help enterprises take preventive measures for upcoming events. The prediction method based on deep neural networks has become the main method used in time series forecasting tasks. In order to improve the performance of the prediction model, this paper designs methods to improve the prediction performance of the model from the perspectives of multi-dimensional, time-domain, and data distribution in multivariate time series data. Using graph neural networks to establish dependencies between multi-dimensional data and perform feature extraction, using dual flow learning networks for time-domain feature extraction, and preventing overfitting of the model during training, and using learnable bias values to mitigate the impact of time series data distribution shift. By conducting experiments on the model in five different application scenarios and comparing it with advanced time series forecasting models, an average improvement of 7.50% was achieved in the MSE index and 6.53% in the MAE index, demonstrating the effectiveness of the model designed in this paper.