Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要