摘要翻译：
在COND-MAT/0110285中提出了一种基于重整化群(RG)的时间序列预测分析算法。本文对其进行显式编码和测试。我们特别选择一些具有每日数据的金融时间序列（股票、指数和商品），并提前一步计算预测。然后，我们构建了一些指标来评价绩效。该算法利用RG方法中固有的自相似性来规定时间序列的未来发展。对于建立赢的交易系统来说，这个属性可能非常有吸引力。我们沿着这个方向讨论了一些相关的问题。尽管目前的性能还有待改进，但该算法似乎对输入参数和不同的过去值序列的各种组合反应很大。这使得它成为一个潜在的很好的候选来探测市场的剧烈波动。最后，我们提到了目前的缺点，并概述了如何改进它们。
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英文标题：
《Using self-similarity and renormalization group to analyze time series》
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作者：
Giovanni Arcioni
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最新提交年份：
2008
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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：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Physics        物理学
二级分类：High Energy Physics - Theory        高能物理-理论
分类描述：Formal aspects of quantum field theory. String theory, supersymmetry and supergravity.
量子场论的形式方面。弦理论，超对称性和超引力。
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英文摘要：
  An algorithm based on Renormalization Group (RG) to analyze time series forecasting was proposed in cond-mat/0110285. In this paper we explicitly code and test it. We choose in particular some financial time series (stocks, indexes and commodities) with daily data and compute one step ahead forecasts. We then construct some indicators to evaluate performances. The algorithm is supposed to prescribe the future development of the time series by using the self-similarity property intrinsically present in RG approach. This property could be potentially very attractive for the purpose of building winning trading systems. We discuss some relevant points along this direction. Although current performances have to be improved the algorithm seems quite reactive to various combinations of input parameters and different past values sequences. This makes it a potentially good candidate to detect sharp market movements. We finally mention current drawbacks and sketch how to improve them.
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PDF链接：
https://arxiv.org/pdf/0805.3213