Transformer network for time series prediction via wavelet packet decomposition - Zhichao Wu; Aiye Shi; Yan Ping Tao (ETRI Journal)
Time series predictions are commonly used in the fields of energy, meteorol-ogy, and finance, among others. The accurate prediction of time series data iscritical for making decisions and planning. In the real world, non-stationarytime series data with statistical properties shift over time, making predictionmore challenging. Although, conventional time series processing methods—such as multi-scale feature extraction or Transformer-based algorithms—produce superior prediction results, when dealing with data that contain morenoise and outliers, the prediction ability of such methods can suffer. Toaddress this problem, we proposed the WPFormer model, which incorporatedtime-frequency analysis into the Transformer architecture to increase thelong-term series prediction accuracy. The model employed wavelet packetdecomposition to identify and eliminate noise efficiently, increasing its immu-nity to interference. We evaluated WPFormer on four publicly available data-sets and compared its performance against the Informer, LogTrans, Reformer,LSTMa, LSTNet, and DeepAR models using MSE and MAE metrics. On aver-age, the WPFormer model surpassed the benchmark models by 16%.