用于长期预测的自监督对比学习
Self-Supervised Contrastive Learning for Long-term Forecasting
AI总结：
针对长期预测中滑动窗口难以捕捉窗口外长期变化的问题，提出结合对比学习和增强分解架构的自监督方法，利用全局自相关构建正负样本对，在九个基准上超越14个基线模型。
摘要：
由于处理长序列的时间和内存复杂性，长期预测面临着独特的挑战。现有方法依赖滑动窗口处理长序列，难以有效捕捉在短窗口内仅被部分捕捉的长期变化（即窗口外变化）。本文引入一种新颖方法，通过采用对比学习和增强的分解架构克服该局限，该架构专门设计用于聚焦长期变化。为此，我们的对比损失整合了整个时间序列中包含的全局自相关，从而以自监督方式促进正负样本对的构建。当与我们的分解网络结合时，对比学习显著提升了长期预测性能。大量实验表明，在九个长期基准的多项实验中，本方法优于14个基线模型，尤其是在需要极长预测输出的挑战性场景中。源代码可在 https://github.com/junwoopark92/Self-Supervised-Contrastive-Forecsating 获取。
英文摘要：
Long-term forecasting presents unique challenges due to the time and memory complexity of handling long sequences. Existing methods, which rely on sliding windows to process long sequences, struggle to effectively capture long-term variations that are partially caught within the short window (i.e., outer-window variations). In this paper, we introduce a novel approach that overcomes this limitation by employing contrastive learning and enhanced decomposition architecture, specifically designed to focus on long-term variations. To this end, our contrastive loss incorporates global autocorrelation held in the whole time series, which facilitates the construction of positive and negative pairs in a self-supervised manner. When combined with our decomposition networks, our contrastive learning significantly improves long-term forecasting performance. Extensive experiments demonstrate that our approach outperforms 14 baseline models in multiple experiments over nine long-term benchmarks, especially in challenging scenarios that require a significantly long output for forecasting. Source code is available at https://github.com/junwoopark92/Self-Supervised-Contrastive-Forecsating.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
