论文标题
时间序列外部回归
Time Series Extrinsic Regression
论文作者
论文摘要
本文研究时间序列外部回归（TSER）：一个回归任务，其目的是学习时间序列与连续标量变量之间的关系；与时间序列分类（TSC）密切相关的任务，该任务旨在了解时间序列与分类类标签之间的关系。该任务概括了时间序列预测（TSF），放宽了预测值是输入序列的未来值或主要取决于最新值的要求。 在本文中，我们激励和研究了这项任务，并在我们组装的19个TSER数据集的新颖档案中基准了TSC算法的现有解决方案和改编。我们的结果表明，与其他TSC算法和最先进的机器学习（ML）算法（如XGBOOST，随机森林和支持载体回归量）相比，与其他TSC算法和最先进的机器学习（ML）算法相比，最先进的TSC算法火箭的总体准确性最高。更重要的是，我们表明在该领域需要进行大量研究以提高ML模型的准确性。我们还发现证据表明，进一步的研究具有改善这些直接基线的良好前景。
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. This task generalizes time series forecasting (TSF), relaxing the requirement that the value predicted be a future value of the input series or primarily depend on more recent values. In this paper, we motivate and study this task, and benchmark existing solutions and adaptations of TSC algorithms on a novel archive of 19 TSER datasets which we have assembled. Our results show that the state-of-the-art TSC algorithm Rocket, when adapted for regression, achieves the highest overall accuracy compared to adaptations of other TSC algorithms and state-of-the-art machine learning (ML) algorithms such as XGBoost, Random Forest and Support Vector Regression. More importantly, we show that much research is needed in this field to improve the accuracy of ML models. We also find evidence that further research has excellent prospects of improving upon these straightforward baselines.
