论文标题
时间序列回归的现代策略
Modern strategies for time series regression
论文作者
论文摘要
本文讨论了几种涉及时间序列数据的回归分析方法的现代方法，其中一些预测变量也由时间索引。我们讨论了最近在机器学习文献中提出的经典统计方法以及方法。比较和对比了这些方法，可以看出，当前可用的方法有优势和缺点。该领域有足够的方法论发展空间。这项工作是由涉及水位的预测与澳大利亚东部含水层中降雨和其他气候变量有关的应用所激发的。
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.
