FAFTransformer: Multivariate time series prediction method based on multi-period feature recombination

Multivariate time series forecasting is widely used in various fields in real life. Many time series prediction models have been proposed. The current forecasting model lacks the mining of correlation between sequences based on different periods and correlation of periodical features between different periods when dealing with data. In this paper, we propose a multivariate data prediction model FAFTransformer based on the reorganization of multi-periodic features, which first extracts the multi-periodic information of the time series using the method of frequency domain analysis. The temporal dependencies within sequences are then captured using convolution based on different periods, and the correlations between sequences are learned by combining the multivariate attention mechanism to obtain the intra-sequence and inter-sequence correlations under the same period. Finally, period fusion is proposed to capture the correlation of period characteristics between different periods. The experimental results show that the model achieves the best results on multiple datasets compared to the latest seven predictive models for time-series data. Based on multi-period feature recombination, a prediction algorithm for multivariate time series is proposed, which fully considers the complex periodicity of each variable and the existence of long-distance correlation between variables. The model achieves the best prediction effect on multiple datasets. image