STFormer: A dual-stage transformer model utilizing spatio-temporal graph embedding for multivariate time series forecasting
Yuteng Xiao,
Zhaoyang Liu,
Hongsheng Yin
et al.
Abstract:Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships… Show more
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