Change Detection in Rough Time Series

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2014
Accession Number
ADA615321

Entities

People

  • Lewis Warren

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Engineered Resilient Systems
  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Change Detection
  • Climate Change
  • Data Mining
  • Data Science
  • Data Sets
  • Detection
  • Detectors
  • False Alarms
  • Fuzzy Sets
  • Information Science
  • Military Operations
  • National Security
  • North Korea
  • Operations Research
  • Security
  • Statistical Analysis
  • Visual Inspection

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.
  • Systems Analysis and Design