CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

TL;DR

CycleNet leverages learnable recurrent cycles to explicitly model periodic patterns in time series data, significantly improving long-term forecasting accuracy across various domains while reducing model complexity.

Contribution

The paper introduces CycleNet, a novel method that explicitly models periodicity with learnable recurrent cycles, enhancing forecasting accuracy and efficiency.

Findings

Achieves state-of-the-art accuracy in electricity, weather, and energy forecasting.

Reduces over 90% of model parameters compared to existing methods.

Improves existing models like PatchTST and iTransformer when integrated.

Abstract

The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (LTSF) tasks. Specifically, we introduce the Residual Cycle Forecasting (RCF) technique, which utilizes learnable recurrent cycles to model the inherent periodic patterns within sequences, and then performs predictions on the residual components of the modeled cycles. Combining RCF with a Linear layer or a shallow MLP forms the simple yet powerful method proposed in this paper, called CycleNet. CycleNet achieves state-of-the-art prediction accuracy in multiple domains including electricity, weather, and energy, while offering significant efficiency advantages by reducing over 90% of the required parameter quantity. Furthermore, as a novel plug-and-play technique, the RCF can also significantly improve the prediction accuracy of existing models, including PatchTST and iTransformer. The source code is available at: https://github.com/ACAT-SCUT/CycleNet.