To address the issues of high computational costs and inability to capture the overall trend of time series using Transformer based method,a combined approach of Transformer and seasonal trend decomposition was proposed.A novel federated long-term time series forecasting algorithm based on decomposed Transformer was introduced,where the decomposition method was employed to capture the global overview of time series.In practical scenarios,time series data originated from multiple different clients.Con-sidering data privacy concerns,a federated learning approach was utilized to obtain an overall optimal forecasting model from multi-ple clients,employing an optimizer based on locally sharpness-aware minimization(SAM)to improve the generalization of the global model.Compared with advanced methods,improvements were observed across multivariate and univariate time series forecas-ting tasks on four benchmark datasets,with the highest performance enhancement reaching 26.9%on the electricity consuming load(ECL)dataset.Experimental results strongly indicated the effectiveness of seasonal trend decomposition and the SAM optimizer in long-term time series forecasting tasks.
维普
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