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.

翻译：长期时间序列预测在各类实际场景中具有重要作用。近期针对长期序列预测的深度学习方法倾向于通过基于分解或基于采样的方法捕捉时间序列的复杂模式。然而，多数提取的模式可能包含不可预测噪声且缺乏良好可解释性。此外，多变量序列预测方法通常忽略各变量的个体特征，这可能影响预测精度。为捕捉时间序列的内在模式，我们提出一种名为多分辨率周期模式网络（MPPN）的新型深度学习网络架构用于长期序列预测。首先构建上下文感知的多分辨率时间序列语义单元，并通过多周期模式挖掘捕获时间序列的关键模式。接着提出通道自适应模块，以捕获多变量对不同模式的感知特性。此外，我们提出基于熵的方法评估时间序列的可预测性，在预测前提供预测准确率的上界。在九个真实世界基准数据集上的实验评估表明，MPPN在长期序列预测任务中显著优于当前最先进的基于Transformer、基于分解和基于采样的方法。