  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
decomposition-based or sampling-based methods. However, most of the extracted
patterns may include unpredictable noise and lack good interpretability.
Moreover, the multivariate series forecasting methods usually ignore the
individual characteristics of each variate, which may affecting 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 perceptions of multivariate
towards different patterns. In addition, we present 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, decomposition-based and sampling-based
methods for long-term series forecasting.
