Abstract
AbstractOrder patterns and permutation entropy have become useful tools for studying biomedical, geophysical or climate time series. Here we study day-to-day market data, and Brownian motion which is a good model for their order patterns. A crucial point is that for small lags (1 up to 6 days), pattern frequencies in financial data remain essentially constant. The two most important order parameters of a time series are turning rate and up-down balance. For change points in EEG brain data, turning rate is excellent while for financial data, up-down balance seems the best. The fit of Brownian motion with respect to these parameters is tested, providing a new version of a forgotten test by Bienaymé.
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference36 articles.
1. Springer seires in synergetics;JM Amigo,2010
2. Amigo J, Keller K, Kurths J (2013) Recent progress in symbolic dynamics and permutation complexity. ten years of permutation entropy. Eur Phys J Spec Top 222:247–257
3. Amigo JM, Keller K, Unakafova VA (2015) Ordinal symbolic analysis and its application to biomedical recordings. Philos Trans R Soc A 373:20140091
4. Bandt C (2015) Permutation entropy and order patterns in long time series. In: Rojas I, Pomares H (eds) Time series analysis and forecasting, contributions to statistics. Springer, New York
5. Bandt C (2017) A new kind of permutation entropy used to classify sleep stages from invisible EEG microstructure. Entropy 19(5):197
Cited by
19 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献