Long-term time series forecasting based on Siamese network: a perspective on few-shot learning

Abstract

The long-term time series forecasting (LTSF) plays a crucial role in various domains, utilizing a large amount of historical data to forecast trends over an extended future time range. However, in real-life scenarios, the performance of LTSF is often hindered by missing data. Few-shot learning aims to address the issue of data scarcity, but there is relatively little research on using few-shot learning to tackle sample scarcity in long-term time series forecasting tasks, and most few-shot learning methods rely on transfer learning. To address this problem, this paper proposes a Siamese network-based time series Transformer (SiaTST) for the task of LTSF in a few-shot setting. To increase the diversity of input scales and better capture local features in time series, we adopt a dual-level hierarchical input strategy. Additionally, we introduce a learnable prediction token (LPT) to capture global features of the time series. Furthermore, a feature fusion layer is utilized to capture dependencies among multiple variables and integrate information from different levels. Experimental results on 7 popular LSTF datasets demonstrate that our proposed model achieves state-of-the-art performance.