Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685926
Title: Analysing and detecting anomalies in sequential time series data
Author: Kong , Xiangzeng
ISNI:       0000 0004 5917 1947
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2015
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Abstract:
Abnormal change detection techniques can be used to solve a range of real world problems but many of the available methods have been developed to address specific application problems, such as change detection of land disturbances, typhoon image analysis and forest fire prediction. The designing of general, scalable and statistically relevant abnormal change detection methods is very impOltant. Computational intelligence and statistical methods provide an effective way of detecting abnormal change in sequential time series data. In this thesis, the aim is to develop methods for detecting and categorizing abnormal changes in sequential data. We propose three new methods to detect changes in data streams: a Geometric Moving Average Martingale (GMAM) method for change detection based on the Martingale theory, two feature extraction methods Piecewise Linear Representation Morphological Feature Points and Piecewise Linear Representation Important Points, and an anomaly detection method in sequential data based on subsequence identification and the weighted local outlier factor method. We also extend the GMAM method and apply it for detecting seismic anomalies in outgoing long-wave radiation data. There are some findings in this thesis. Firstly, there are two components underpinning the GMAM method. One is the exponential weighting of observations which has the capability of reducing false changes. Another is the use of the GMAM value for hypothesis testing. Secondly, the proposed piecewise representation method based on morphological feature points and important points can extract the features of time series data and help the weighted local outlier factor method to find the anomalies of time series. Thirdly, the weighted local outlier factor method can obtain higher accuracy when applied to 17 data sets than the LOF method and Hot SAX methods. Finally, an extension of the GMAM method to the Average GMAM method (AG) has been applied to analyse seismic anomalies within Outgoing Long-wave Radiation (OLR) data observed by Satellites from 2006 to 2015 for the two recent Wenchuan and Lushan earthquakes and four comparative study areas: Wenchuan, Puer, Beijing and Northeastern areas. The Yushu earthquake and Hetian earthquake have also been examined. The experimental results show that the proposed AG method can effectively discover abnormal changes within OLR data and that there are large AG values in the pre- and post-occurrence of the earthquakes in these areas, which could be viewed as seismic anomalies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.685926  DOI: Not available
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