Accurate forecasting of long-term time series has important applications for decision making and planning. However, it remains challenging to capture the long-term dependencies in time series data. To better extract long-term dependencies, We propose Multi Scale Dilated Convolution Network (MSDCN), a method that utilizes a shallow dilated convolution architecture to capture the period and trend characteristics of long time series. We design different convolution blocks with exponentially growing dilations and varying kernel sizes to sample time series data at different scales. Furthermore, we utilize traditional autoregressive model to capture the linear relationships within the data. To validate the effectiveness of the proposed approach, we conduct experiments on eight challenging long-term time series forecasting benchmark datasets. The experimental results show that our approach outperforms the prior state-of-the-art approaches and shows significant inference speed improvements compared to several strong baseline methods.

翻译：准确预测长期时间序列对决策和规划具有重要应用价值。然而，捕捉时间序列数据中的长期依赖关系仍具挑战性。为更好提取长期依赖特征，本文提出多尺度扩张卷积网络（MSDCN），该方法采用浅层扩张卷积架构来捕捉长时间序列的周期与趋势特征。我们设计了具有指数增长扩张率和不同卷积核大小的多个卷积模块，用于在不同尺度上对时间序列数据进行采样。此外，我们利用传统自回归模型来捕捉数据中的线性关系。为验证所提方法的有效性，我们在八个具有挑战性的长期时间序列预测基准数据集上进行了实验。实验结果表明，本方法优于现有最先进方法，并且与多个强基线方法相比，推理速度显著提升。