一种基于时-频特征融合的多变量时间序列预测模型OA

To address the problem in multi-variate time series prediction that existing methods were difficult to effectively identify and capture long-short-term features and periodic characteristics simultaneously,a multi-variate time series prediction model based on temporal-frequency feature fusion was proposed.The model was composed of an input module,a long-short-term feature extraction module,a periodic feature extraction module,and a fusion output module.The input module was used to preprocess data through reversible instance normalization(RevIN);the long-short-term feature extraction module was combined with Mamba and TCN networks in the temporal domain to capture the long-term and short-term dependencies of the sequence,respectively;the periodic feature extraction module was used to capture the periodic characteristics of the sequence through a frequency-domain attention mechanism;the fusion output module fused the temporal and frequency-domain features,and the prediction result was output through a fully connected layer.Experiments on seven public datasets show that the average prediction accuracy of this model is increased by 6.63％,8.55％,18.64％,48.07％,20.71％,9.09％,16.44％,10.66％,and 1.99％compared with that of the iTransformer,PatchTST,FEDformer,Crossformer,TiDE,RLinear,DLinear,TimesNet,and MICN models,respectively.The model is able to maintain high prediction accuracy while also having high computational efficiency.