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However, traditional RNN\u2010based approaches for predicting multivariate time series are still facing challenges, as time series are often related to each other and historical observations in real\u2010world applications. To address this limitation, this paper proposes a spatiotemporal self\u2010attention mechanism\u2010based LSTNet, which is a multivariate time series forecasting model. The proposed model leverages two self\u2010attention strategies, spatial and temporal self\u2010attention, to focus on the most relevant information among time series. The spatial self\u2010attention is used to discover the dependences between variables, while temporal attention is employed to capture the relationship among historical observations. Moreover, a standard deviation term is added to the objective function to track multivariate time series effectively. To evaluate the proposed method\u2019s performance, extensive experiments are conducted on multiple benchmarked datasets. The experimental results show that the proposed method outperforms several baseline methods significantly. 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