  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.
