Exploring Progress in Multivariate Time Series Forecasting:
  Comprehensive Benchmarking and Heterogeneity Analysis
Zezhi Shao Fei Wang Yongjun Xu Wei Wei Chengqing Yu Zhao Zhang Di Yao Guangyin Jin Xin Cao Gao Cong Christian S. Jensen Xueqi Cheng
Multivariate Time Series (MTS) widely exists in real-word complex systems,
such as traffic and energy systems, making their forecasting crucial for
understanding and influencing these systems. Recently, deep learning-based
approaches have gained much popularity for effectively modeling temporal and
spatial dependencies in MTS, specifically in Long-term Time Series Forecasting
(LTSF) and Spatial-Temporal Forecasting (STF). However, the fair benchmarking
issue and the choice of technical approaches have been hotly debated in related
work. Such controversies significantly hinder our understanding of progress in
this field. Thus, this paper aims to address these controversies to present
insights into advancements achieved. To resolve benchmarking issues, we
introduce BasicTS, a benchmark designed for fair comparisons in MTS
forecasting. BasicTS establishes a unified training pipeline and reasonable
evaluation settings, enabling an unbiased evaluation of over 30 popular MTS
forecasting models on more than 18 datasets. Furthermore, we highlight the
heterogeneity among MTS datasets and classify them based on temporal and
spatial characteristics. We further prove that neglecting heterogeneity is the
primary reason for generating controversies in technical approaches. Moreover,
based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct
an exhaustive and reproducible performance and efficiency comparison of popular
models, providing insights for researchers in selecting and designing MTS
forecasting models.