英文标题：
《The ETS challenges: a machine learning approach to the evaluation of
  simulated financial time series for improving generation processes》
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作者：
Javier Franco-Pedroso, Joaquin Gonzalez-Rodriguez, Maria Planas, Jorge
  Cubero, Rafael Cobo, Fernando Pablos
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最新提交年份：
2018
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英文摘要：
  This paper presents an evaluation framework that attempts to quantify the \"degree of realism\" of simulated financial time series, whatever the simulation method could be, with the aim of discover unknown characteristics that are not being properly reproduced by such methods in order to improve them. For that purpose, the evaluation framework is posed as a machine learning problem in which some given time series examples have to be classified as simulated or real financial time series. The \"challenge\" is proposed as an open competition, similar to those published at the Kaggle platform, in which participants must send their classification results along with a description of the features and the classifiers used. The results of these \"challenges\" have revealed some interesting properties of financial data, and have lead to substantial improvements in our simulation methods under research, some of which will be described in this work.
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中文摘要：
本文提出了一个评估框架，试图量化模拟金融时间序列的“真实度”，无论模拟方法是什么，目的是发现这些方法无法正确再现的未知特征，以改进它们。为此，评估框架被视为一个机器学习问题，其中一些给定的时间序列示例必须分类为模拟或真实的金融时间序列。“挑战赛”是一项公开比赛，类似于在Kaggle平台上发布的比赛，参赛者必须发送其分类结果以及所使用的特征和分类器的描述。这些“挑战”的结果揭示了金融数据的一些有趣特性，并导致我们正在研究的模拟方法有了实质性的改进，其中一些将在本文中描述。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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