Time series forecasting is crucial and challenging in the real world. The
recent surge in interest regarding time series foundation models, which cater
to a diverse array of downstream tasks, is noteworthy. However, existing
methods often overlook the multi-scale nature of time series, an aspect crucial
for precise forecasting. To bridge this gap, we propose HiMTM, a hierarchical
multi-scale masked time series modeling method designed for long-term
forecasting. Specifically, it comprises four integral components: (1)
hierarchical multi-scale transformer (HMT) to capture temporal information at
different scales; (2) decoupled encoder-decoder (DED) forces the encoder to
focus on feature extraction, while the decoder to focus on pretext tasks; (3)
multi-scale masked reconstruction (MMR) provides multi-stage supervision
signals for pre-training; (4) cross-scale attention fine-tuning (CSA-FT) to
capture dependencies between different scales for forecasting. Collectively,
these components enhance multi-scale feature extraction capabilities in masked
time series modeling and contribute to improved prediction accuracy. We conduct
extensive experiments on 7 mainstream datasets to prove that HiMTM has obvious
advantages over contemporary self-supervised and end-to-end learning methods.
The effectiveness of HiMTM is further showcased by its application in the
industry of natural gas demand forecasting.

通过使用多尺度层次掩蔽时间序列建模方法，HiMTM 在提升多尺度特征提取能力的同时，通过实验证明其在时间序列预测准确性上具有明显优势，并在天然气需求预测领域得到了有效应用。