Multi-Layer Seasonal Perception Network for Time Series Forecasting

Seasonal time series contain rich long-term dependencies. How to make good use of the seasonal information to predict the future is still a challenging problem. In this paper, we propose a neural network model called Multilayer Seasonal Perception Network (MSPNet) to predict seasonal time series. Firstly, we propose the idea of seasonal alignment, which converts univariate time series into multivariate time series, in order to capture seasonal features more effectively. Secondly, we extract the seasonal features and historical dependencies, using the Multi-layer Seasonal Perception Attention. Finally, we combine the obtained nonlinear features with linear features to conduct the final prediction. Experimental verification shows that the proposed MSPNet model is significantly superior to the baseline methods on multiple public datasets. The source code and datasets are available at https://github.com/MasterofEating/MSPNet