基于TA-Informer模型的多元长期时间序列预测研究OA

In multivariate long-term time series forecasting,the feature redundancy and long-term dependency of the data are difficult to capture,which becomes a key problem affecting the forecasting accuracy.In order to improve the multi-variate long-term time series prediction accuracy,a multivariate long-term time series prediction model based on TA-Informer is proposed.Firstly,the model uses temporal convolutional network(TCN)to extract features from multi-variate long-term time series for capturing long-term dependencies.Then,the model feeds the extracted features into an adaptive sparse self-attention(ASSA)to eliminate redundant features and enhance the important features.Finally,the model feeds the enhanced important features into the Informer module to realize the multivariate long-term time series prediction task.The experimental results show that TA-Informer reduces the MSE on six public datasets by 57.5%,25.8%,50.3%,60%,48.1%and 45.2%,respectively,compared with the benchmark model Informer,which reflects the effectiveness and feasibility of the scheme.