CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System

arXiv:2207.07827v4 Announce Type: replace
Abstract: Long-term time-series forecasting (LTSF) plays a crucial role in various practical applications. Transformer and its variants have become the de facto backbone for LTSF, offering exceptional capabilities in processing long sequence data. However, existing Transformer-based models, such as Fedformer and Informer, often achieve their best performances on validation sets after just a few epochs, indicating potential underutilization of the Transformer's capacity. One of the reasons that contribute to this overfitting is data redundancy arising from …

abstract applications arxiv become capabilities cs.cv cs.lg data data redundancy forecasting long-term practical processing redundancy role series time series time series forecasting transformer type variants