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A modified wavelet-based method for detection of outliers in time series | ||
| Earth Observation and Geomatics Engineering | ||
| مقاله 8، دوره 3، شماره 1، شهریور 2019، صفحه 77-83 اصل مقاله (966.4 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2019.285487.1054 | ||
| نویسندگان | ||
| Amirreza Moradi* 1؛ Sajjad Asiaei Mojarad2 | ||
| 1Department of Surveying Engineering, Arak University of Technology, DaneshgahSt., P.O. Box 38135-1177, Arak, Iran | ||
| 2Department of Surveying Engineering, Arak University of Technology , Arak , Daneshgah street , Iran | ||
| چکیده | ||
| As a multi-resolution analysis, wavelet transformation tool has been used to detect contingent outliers in time series data with no need to specify a model for the data. The objective of this article is to design an orthonormal wavelet system that optimizes the wavelet-based outlier detection procedure. In addition, we show that regardless of the selected base functions, the existing wavelet-based methods extract two adjacent suspicious observations so that probably one of them is an outlier. Therefore, we modify the wavelet-based outlier detection scheme by introducing a transformation matrix consisting of our designed wavelet filters that can be used to detect outlying observations without the above-mentioned ambiguity. In a numerical example, a sample observation vector is analyzed using our scheme. At the same time, a robust statistical approach- modified z-score method- has been used to evaluate the capability of our desired wavelet-based procedure. The results were completely reliable and comparable. | ||
| کلیدواژه‌ها | ||
| Wavelet transform؛ Outlier detection؛ time series | ||
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آمار تعداد مشاهده مقاله: 370 تعداد دریافت فایل اصل مقاله: 371 |
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