Change Detection in Rough Time Series
DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION EDINBURGH (AUSTRALIA) INTELLIGENCE SURVEILLANCE AND RECONNAISSANCE DIV
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A discrete time series may characteristically have high noise levels resulting in a rough or jagged distribution which can present significant challenges to conventional statistical tracking techniques. To address this problem the proposed method applies hybrid fuzzy statistical techniques to series granules instead of to individual measures. After detailing the method and its rationale, three examples demonstrate the robust nature of the proposed fuzzy tracking signal which leads to a minimal number of false alarms caused by isolated spikes. The examples demonstrate the effectiveness of this tracking signal for promptly identifying significant pattern changes in rough time series as can be encountered in data sets used for various types of Defence decision making.
- Numerical Mathematics