This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.

中文翻译：

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时间序列回归的现代策略

本文讨论了涉及时间序列数据的几种现代回归分析方法，其中一些预测变量也按时间进行索引。我们讨论了经典的统计方法以及最近在机器学习文献中提出的方法。对这些方法进行比较和对比，将会看到大多数当前可用方法的优点和缺点。在该领域有足够的方法发展空间。这项工作的动机是一个应用程序，该应用程序涉及预测澳大利亚东部含水层中作为降雨量和其他气候变量的函数的水位。