英文标题：
《Clustering Financial Time Series: How Long is Enough?》
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作者：
Gautier Marti, S\\\'ebastien Andler, Frank Nielsen, Philippe Donnat
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最新提交年份：
2016
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英文摘要：
  Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values is statistically consistent. Then, we also give a first empirical answer to the much debated question: How long should the time series be? If too short, the clusters found can be spurious; if too long, dynamics can be smoothed out.
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中文摘要：
研究人员利用30天到几年的每日收益率作为源数据，根据它们的相关性对金融时间序列进行聚类。本文建立了一个统计框架来研究这种做法的有效性。我们首先表明，从观测值中聚类相关随机变量在统计学上是一致的。然后，我们也给出了一个备受争议的问题的第一个实证答案：时间序列应该是多长？如果太短，发现的簇可能是虚假的；如果时间太长，动态可以被平滑。
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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