MPPN：用于长期时间序列预测的多分辨率周期模式网络
MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting
AI总结：
针对长期时间序列预测中现有方法模式噪声多、可解释性差、忽略多变量个体特征的问题，提出MPPN架构，通过多分辨率语义单元、多周期模式挖掘、通道自适应模块及熵基可预测性评估方法，在9个基准上性能超越现有最优方法。
摘要：
长期时间序列预测在各类真实场景中发挥着重要作用。现有的长期序列预测深度学习方法多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，但提取出的大多数模式可能包含不可预测的噪声，且可解释性不佳。此外，多变量序列预测方法通常会忽略每个变量的个体特征，这可能会影响预测精度。为捕捉时间序列的内在模式，我们提出了一种名为多分辨率周期模式网络（Multi-resolution Periodic Pattern Network，MPPN）的新型深度学习网络架构，用于长期序列预测。我们首先构建时间序列的上下文感知多分辨率语义单元，并采用多周期模式挖掘来捕捉时间序列的关键模式；随后提出通道自适应模块，以捕捉多变量对不同模式的感知。此外，我们还提出了一种基于熵的方法，用于评估时间序列的可预测性，并在预测前给出预测精度的上限。我们在9个真实世界基准上开展的实验评估表明，在长期序列预测任务中，MPPN的性能显著优于当前最优的基于Transformer、基于分解和基于采样的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
