一种用于线性多变量预测的多任务学习方法
A Multi-Task Learning Approach to Linear Multivariate Forecasting
AI总结：
本文将多变量时间序列预测建模为多任务学习，提出基于相关性聚类分组变量、按预测误差缩放梯度，并在线性 MTLinear 框架中实现性能相当或更优。
摘要：
多变量时间序列数据的准确预测在许多工程和科学应用中都很重要。近期最先进的研究忽略了变量之间的相互关系，而是对每个变量独立使用其模型。这引发了若干与恰当建模多变量数据相关的研究问题。在本工作中，我们建议将多变量预测视为一个多任务学习问题，通过考虑任务梯度之间的夹角及其平衡来促进预测分析。为此，我们分析线性模型以刻画任务的行为。我们的分析表明，可以通过将相似变量分组在一起来定义任务，而我们通过一种依赖基于相关性的相似性的简单聚类来实现这一点。此外，为了平衡任务，我们根据任务的预测误差对梯度进行缩放。随后，在我们的 MTLinear 框架内，使用线性模型求解每个任务。我们在具有挑战性的基准上评估了所提方法，并与强基线进行比较，结果表明该方法在多变量预测问题上取得了相当或更好的结果。实现可在 https://github.com/azencot-group/MTLinear 获取。
英文摘要：
Accurate forecasting of multivariate time series data is important in many engineering and scientific applications. Recent state-of-the-art works ignore the inter-relations between variates, using their model on each variate independently. This raises several research questions related to proper modeling of multivariate data. In this work, we propose to view multivariate forecasting as a multi-task learning problem, facilitating the analysis of forecasting by considering the angle between task gradients and their balance. To do so, we analyze linear models to characterize the behavior of tasks. Our analysis suggests that tasks can be defined by grouping similar variates together, which we achieve via a simple clustering that depends on correlation-based similarities. Moreover, to balance tasks, we scale gradients with respect to their prediction error. Then, each task is solved with a linear model within our MTLinear framework. We evaluate our approach on challenging benchmarks in comparison to strong baselines, and we show it obtains on-par or better results on multivariate forecasting problems. The implementation is available at: https://github.com/azencot-group/MTLinear
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。
