BasisFormer：基于注意力、具有可学习且可解释基的时间序列预测
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
AI总结：
本文提出BasisFormer端到端预测架构，通过自监督对比学习获得可解释基，并以双向交叉注意力和预测模块整合基，在六个数据集上显著提升单、多变量时间序列预测性能。
摘要：
基已成为现代基于深度学习的时间序列预测模型不可或缺的组成部分，因为它们能够充当特征提取器或未来参考。为了有效，基必须针对特定的时间序列数据集进行定制，并且与该集合中的每条时间序列表现出明显的相关性。然而，当前最先进的方法在同时满足这两项要求方面能力有限。为应对这一挑战，我们提出BasisFormer，这是一种利用可学习且可解释基的端到端时间序列预测架构。该架构包含三个组件：首先，我们通过自适应自监督学习获取基，该学习将时间序列的历史部分和未来部分视为两个不同视图，并采用对比学习。接着，我们设计一个Coef模块，通过双向交叉注意力计算历史视图中时间序列与基之间的相似系数。最后，我们提出一个Forecast模块，根据相似系数选择并整合未来视图中的基，从而生成准确的未来预测。通过在六个数据集上的大量实验，我们证明BasisFormer在单变量和多变量预测任务上分别比此前最先进方法优11.04%和15.78%。代码位于：https://github.com/nzl5116190/Basisformer。
英文摘要：
Bases have become an integral part of modern deep learning-based models for time series forecasting due to their ability to act as feature extractors or future references. To be effective, a basis must be tailored to the specific set of time series data and exhibit distinct correlation with each time series within the set. However, current state-of-the-art methods are limited in their ability to satisfy both of these requirements simultaneously. To address this challenge, we propose BasisFormer, an end-to-end time series forecasting architecture that leverages learnable and interpretable bases. This architecture comprises three components: First, we acquire bases through adaptive self-supervised learning, which treats the historical and future sections of the time series as two distinct views and employs contrastive learning. Next, we design a Coef module that calculates the similarity coefficients between the time series and bases in the historical view via bidirectional cross-attention. Finally, we present a Forecast module that selects and consolidates the bases in the future view based on the similarity coefficients, resulting in accurate future predictions. Through extensive experiments on six datasets, we demonstrate that BasisFormer outperforms previous state-of-the-art methods by 11.04\% and 15.78\% respectively for univariate and multivariate forecasting tasks. Code is available at: \url{https://github.com/nzl5116190/Basisformer}
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。
