A Long-term Time Series Forecasting method with Multiple Decomposition

In various real-world applications such as weather forecasting, energy consumption planning, and traffic flow prediction, time serves as a critical variable. These applications can be collectively referred to as time-series prediction problems. Despite recent advancements with Transformer-based solutions yielding improved results, these solutions often struggle to capture the semantic dependencies in time-series data, resulting predominantly in temporal dependencies. This shortfall often hinders their ability to effectively capture long-term series patterns. In this research, we apply time-series decomposition to address this issue of long-term series forecasting. Our method involves implementing a time-series forecasting approach with deep series decomposition, which further decomposes the long-term trend components generated after the initial decomposition. This technique significantly enhances the forecasting accuracy of the model. For long-term time-series forecasting (LTSF), our proposed method exhibits commendable prediction accuracy on four publicly available datasets-Weather, Electricity, Traffic, ILI-when compared to prevailing methods. The code for our method is accessible at https://github.com/wangyang970508/LSTF_MD.