MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting
This work solves forecasting challenges for applications requiring efficient and interpretable predictions, though it appears incremental as it builds on convolution-based pattern methods.
The paper tackles the problem of time series forecasting by proposing MPR-Net, which addresses high computational complexity and lack of interpretability in existing models, achieving state-of-the-art performance with linear time complexity and interpretable forecasting across multiple real datasets.
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.