From Similarity to Superiority: Channel Clustering for Time Series
  Forecasting
Jialin Chen Jan Eric Lenssen Aosong Feng Weihua Hu Matthias Fey Leandros Tassiulas Jure Leskovec Rex Ying
Time series forecasting has attracted significant attention in recent
decades. Previous studies have demonstrated that the Channel-Independent (CI)
strategy improves forecasting performance by treating different channels
individually, while it leads to poor generalization on unseen instances and
ignores potentially necessary interactions between channels. Conversely, the
Channel-Dependent (CD) strategy mixes all channels with even irrelevant and
indiscriminate information, which, however, results in oversmoothing issues and
limits forecasting accuracy. There is a lack of channel strategy that
effectively balances individual channel treatment for improved forecasting
performance without overlooking essential interactions between channels.
Motivated by our observation of a correlation between the time series model's
performance boost against channel mixing and the intrinsic similarity on a pair
of channels, we developed a novel and adaptable Channel Clustering Module
(CCM). CCM dynamically groups channels characterized by intrinsic similarities
and leverages cluster identity instead of channel identity, combining the best
of CD and CI worlds. Extensive experiments on real-world datasets demonstrate
that CCM can (1) boost the performance of CI and CD models by an average margin
of 2.4% and 7.2% on long-term and short-term forecasting, respectively; (2)
enable zero-shot forecasting with mainstream time series forecasting models;
(3) uncover intrinsic time series patterns among channels and improve
interpretability of complex time series models.