Time series online forecasting based on sequence decomposition learning networks

Yunpeng Ma, Chenheng Xu, Hua Wang, Shengkai Liu, Xiaoying Gu

Abstract: Highlights•For solving the problem of time series online forecasting, a kind of novel model that does not need to continuously capture specific time window features, call Sequence Decomposition Learning Networks (SDLN) is proposed in this paper. It is applied to eight publicly available time series datasets. The experimental results show that it not only has a forecasting accuracy of 10-2 to 10-4, but also has a forecasting time of 10-2s.