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.

通过使用浅层扩张卷积架构以捕捉长时间序列的周期性和趋势特征，我们提出了多尺度扩张卷积网络 (MSDCN) 的方法，并设计了具有指数增长扩张和不同卷积核大小的不同卷积块来在不同尺度上采样时间序列数据。此外，我们利用传统的自回归模型来捕捉数据内的线性关系。通过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实验证明，我们的方法优于先前的最先进方法，并与几种强基准方法相比显示出显著的推理速度改进。