From Similarity to Superiority: Channel Clustering for Time Series Forecasting

系列(地层学) 聚类分析 相似性(几何) 计算机科学 数据挖掘 时间序列 人工智能 频道(广播) 模式识别(心理学) 计量经济学 机器学习 数学 电信 地质学 古生物学 图像(数学)
作者
Jialin Chen,Jan Eric Lenssen,Aosong Feng,Weihua Hu,Matthias Fey,Leandros Tassiulas,Jure Leskovec,Rex Ying
出处
期刊:Cornell University - arXiv 被引量:2
标识
DOI:10.48550/arxiv.2404.01340
摘要

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.
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