摘要翻译：
时间序列聚类的数据挖掘技术在许多领域得到了广泛的应用。然而，作为一种无监督学习方法，它要求做出的选择受到所涉及的数据性质的影响。本文的目的是验证时间序列聚类方法在宏观经济研究中的有效性，并开发出最适合的方法。通过对各种可能性的广泛检验，我们得到了一种特别适合于聚类宏观经济变量的相异测度（基于压缩的相异测度，简称CDM）的选择。我们检查结果在时间上是稳定的，并反映危机等大规模现象。我们还成功地将我们的发现应用于国民经济分析，特别是识别它们的结构关系。
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英文标题：
《Clustering Macroeconomic Time Series》
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作者：
Iwo Augusty\'nski, Pawe{\l} Lasko\'s-Grabowski
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  The data mining technique of time series clustering is well established in many fields. However, as an unsupervised learning method, it requires making choices that are nontrivially influenced by the nature of the data involved. The aim of this paper is to verify usefulness of the time series clustering method for macroeconomics research, and to develop the most suitable methodology.   By extensively testing various possibilities, we arrive at a choice of a dissimilarity measure (compression-based dissimilarity measure, or CDM) which is particularly suitable for clustering macroeconomic variables. We check that the results are stable in time and reflect large-scale phenomena such as crises. We also successfully apply our findings to analysis of national economies, specifically to identifying their structural relations.
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PDF链接：
https://arxiv.org/pdf/1807.04004