摘要翻译：
最近出现的许多应用都要求研究数据流，即潜在的无限大的实时更新的信息流。当观察到多个协同演化的数据流时，一个重要的任务是确定这些数据流如何相互依赖，在不施加任何约束这种依赖的概率规律的情况下考虑动态依赖模式。本文认为，灵活最小二乘(FLS)是一种适应时变回归系数的普通最小二乘的惩罚版本，可以成功地应用于这种情况。我们的激励应用是统计套利，一种利用金融数据流中检测到的模式的投资策略。我们证明了FLS与著名的Kalman滤波方程在代数上是等价的，并利用这一等价性来更好地理解FLS并提出了一种更有效的算法。本文报告了一个基于FLS的标准普尔500期货指数算法交易系统的实验结果。
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英文标题：
《Flexible least squares for temporal data mining and statistical
  arbitrage》
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作者：
Giovanni Montana, Kostas Triantafyllopoulos, and Theodoros Tsagaris
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最新提交年份：
2007
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分类信息：

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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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  A number of recent emerging applications call for studying data streams, potentially infinite flows of information updated in real-time. When multiple co-evolving data streams are observed, an important task is to determine how these streams depend on each other, accounting for dynamic dependence patterns without imposing any restrictive probabilistic law governing this dependence. In this paper we argue that flexible least squares (FLS), a penalized version of ordinary least squares that accommodates for time-varying regression coefficients, can be deployed successfully in this context. Our motivating application is statistical arbitrage, an investment strategy that exploits patterns detected in financial data streams. We demonstrate that FLS is algebraically equivalent to the well-known Kalman filter equations, and take advantage of this equivalence to gain a better understanding of FLS and suggest a more efficient algorithm. Promising experimental results obtained from a FLS-based algorithmic trading system for the S&P 500 Futures Index are reported.
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PDF链接：
https://arxiv.org/pdf/0709.3884