MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting

Xing Wang, Zhendong Wang, Kexin Yang, zhiyan song, Lin Zhu, Chao Deng, Junlan Feng

Keywords: Long-term time series forecasting, Multi-resolution periodic pattern, Channel adaption, Multivariate time series prediction.

TL;DR: This paper presents a novel Multi-Resolution Periodic Pattern Network for long-term time series forecasting, achieving significant accuracy improvements.

Abstract: Long-term time series forecasting plays an important role in various real-world scenarios. Recent deep learning methods for long-term series forecasting tend to capture the intricate patterns of time series by Transformer-based or sampling-based methods. However, most of the extracted patterns are relatively simplistic and may include unpredictable noise. Moreover, the multivariate series forecasting methods usually ignore the individual characteristics of each variate, which may affect the prediction accuracy. To capture the intrinsic patterns of time series, we propose a novel deep learning network architecture, named Multi-resolution Periodic Pattern Network (MPPN), for long-term series forecasting. We first construct context-aware multi-resolution semantic units of time series and employ multi-periodic pattern mining to capture the key patterns of time series. Then, we propose a channel adaptive module to capture the multivariate perceptions towards different patterns. In addition, we adopt an entropy-based method for evaluating the predictability of time series and providing an upper bound on the prediction accuracy before forecasting. Our experimental evaluation on nine real-world benchmarks demonstrated that MPPN significantly outperforms the state-of-the-art Transformer-based, sampling-based and pre-trained methods for long-term series forecasting.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 5436