Multi-variate time series forecasting based on improved Transformer model
In wireless data transmission,issues such as packet loss and missing data due to environmental inter-ference and network congestion significantly affect the stability of time series prediction.To address this problem,a time series prediction model called TFKNet is proposed.This model is based on the Transformer method for multidimensional time series data prediction.It integrates Temporal Convolutional Networks(TCN)and Fourier frequency feature extraction techniques into the traditional Transformer model,enhancing the model's ability to capture local features and extract multi-frequency features.Additionally,Kernel Attention Networks(KAN)are introduced to improve the model's predictive performance.Experimental results demonstrate that,compared to traditional methods such as Transformer,Informer,Reformer,and Autoformer,the TFKNet model achieves an average reduction in prediction error(MAE)by 0.052 2,0.111 7,0.120 9,and 0.192 2,respectively,in long-term time series prediction tasks.
data predictionTransformerTCNKANFourier frequency features
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