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.

本研究提出了一种完整的解决方案，包括特征提取和目标预测，旨在解决多变量长期时间序列预测中的空间特征提取和不同预测时期的一致性问题。新的方法称为 “时空编码串联变压器（Stecformer）”，其采用高效的时空编码提取器和级联解码预测器来改善基线模型的性能，并在五个基准数据集上实现了与基线模型可比较的最先进性能。