MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting

TL;DR

MPR-Net is a novel time series forecasting model that adaptively decomposes multi-scale patterns, uses pattern reproduction for forecasting, and reconstructs future series, achieving state-of-the-art performance with interpretability and linear complexity.

Contribution

The paper introduces MPR-Net, a new forecasting model that combines adaptive pattern decomposition, pattern reproduction, and reconstruction, addressing computational complexity and interpretability issues of prior models.

Findings

Achieves state-of-the-art forecasting accuracy on multiple datasets.

Maintains linear time complexity for efficient computation.

Demonstrates strong generalization and robustness across tasks.

Abstract

Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging. The research challenge lies in identifying effective patterns in historical series and applying them to future forecasting. Advanced models based on point-wise connected MLP and Transformer architectures have strong fitting power, but their secondary computational complexity limits practicality. Additionally, those structures inherently disrupt the temporal order, reducing the information utilization and making the forecasting process uninterpretable. To solve these problems, this paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable. By carrying out sufficient experiments on more than ten real data sets of both short and long term forecasting tasks, MPR-Net achieves the state of the art forecasting performance, as well as good generalization and robustness performance.