Effects of trends and seasonalities on robustness of the Hurst parameter estimators
dc.contributor.author | Ye, Xianming | |
dc.contributor.author | Xia, Xiaohua | |
dc.contributor.author | Zhang, Jiangfeng | |
dc.contributor.author | Chen, Yangquan | |
dc.contributor.email | xianming.ye@up.ac.za | en_US |
dc.date.accessioned | 2013-10-03T12:25:02Z | |
dc.date.available | 2013-10-03T12:25:02Z | |
dc.date.issued | 2013 | |
dc.description.abstract | Long-range dependence (LRD) is discovered in time series arising from different fields, especially in network traffic and econometrics. Detecting the presence and the intensity of LRD plays a crucial role in time-series analysis and fractional system identification. The existence of LRD is usually indicated by the Hurst parameters. Up to now, many Hurst parameter estimators have been proposed in order to identify the LRD property involved in a time series. Since different estimators have different accuracy and robustness performances, in this study, 13 most popular Hurst parameter estimators are summarised and their estimation performances are investigated. LRD processes with known Hurst parameters are generated as the control data set for the robustness evaluation. In addition, three types of LRD processes are also obtained as the test signals by adding noises in terms of means, trends and seasonalities to the control data set. All 13 Hurst parameter estimators are applied to these LRD processes to estimate the existing Hurst parameters. The estimation results are documented and quantified by the standard errors. Conclusions of the accuracy and robustness performances of the estimators are drawn by comparing the estimation results. | en_US |
dc.description.librarian | hb2013 | en_US |
dc.description.sponsorship | The National Hub for the Postgraduate Programme in Energy Efficiency and Demand Side Management at the University of Pretoria. | en_US |
dc.description.uri | http://www.ietdl.org | en_US |
dc.identifier.citation | Ye, X, Xia, X, Zhang, J & Chen, Y 2013, 'Effects of trends and seasonalities on robustness of the Hurst parameter estimators', IET Signal Processing, vol. 6, no. 9, pp. 849-856. | en_US |
dc.identifier.issn | 1751-9675(print) | |
dc.identifier.issn | 1751-9683 (online) | |
dc.identifier.other | 10.1049/iet-spr.2012.0050 | |
dc.identifier.uri | http://hdl.handle.net/2263/31900 | |
dc.language.iso | en | en_US |
dc.publisher | Institution of Engineering and Technology | en_US |
dc.rights | Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication inIET Signal Processing and is subject to Institution of Engineering. This paper is a postprint of a paper submitted to and accepted for publication in IET Signal Processing and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library. | en_US |
dc.subject | Hurst parameters | en_US |
dc.subject | fGn | en_US |
dc.subject | Robustness | en_US |
dc.subject | Long-range dependence (LRD) | en_US |
dc.title | Effects of trends and seasonalities on robustness of the Hurst parameter estimators | en_US |
dc.type | Postprint Article | en_US |