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Using self-similarity and renormalization group to analyze time series

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  • Giovanni Arcioni

Abstract

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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  • Giovanni Arcioni, 2008. "Using self-similarity and renormalization group to analyze time series," Papers 0805.3213, arXiv.org.
    • Handle: RePEc:arx:papers:0805.3213
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    References listed on IDEAS

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    1. S. Gluzman & V. I. Yukalov, 1997. "Renormalization Group Analysis of October Market Crashes," Papers cond-mat/9710336, arXiv.org, revised Apr 1998.
    2. S. Gluzman & V. I. Yukalov, 1998. "Booms and Crashes in Self-Similar Markets," Papers cond-mat/9810092, arXiv.org.
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