  Multivariate long-term time series forecasting is of great application across
many domains, such as energy consumption and weather forecasting. With the
development of transformer-based methods, the performance of multivariate
long-term time series forecasting has been significantly improved, however, the
study of spatial features extracting in transformer-based model is rare and the
consistency of different prediction periods is unsatisfactory due to the large
span. In this work, we propose a complete solution to address these problems in
terms of feature extraction and target prediction. For extraction, we design an
efficient spatio-temporal encoding extractor including a semi-adaptive graph to
acquire sufficient spatio-temporal information. For prediction, we propose a
Cascaded Decoding Predictor (CDP) to strengthen the correlation between
different intervals, which can also be utilized as a generic component to
improve the performance of transformer-based methods. The proposed method,
termed as Spatio-temporal Encoding Cascaded Transformer (Stecformer), achieving
a notable gap over the baseline model and is comparable with the
state-of-the-art performance of transformer-based methods on five benchmark
datasets. We hope our attempt will serve as a regular configuration in
multivariate long-term time series forecasting in the future.
