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Novelty Detection in Time Series

Spiekstra, J.S. (2000) Novelty Detection in Time Series. Master's Thesis / Essay, Computing Science.

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This report discusses novelty detection in time series. Particularly novelty detection with clustering and time series prediction using a neural network. The question discussed in this report is weather a clustering of static relations or the modelling of dynamics in time series provides a better approach for novelty detection.This question originates from a problem of novelty detection in radiotelescope data gathered by the NFRAJAstron and is posed as a hypothesis after introducing novelty detection in chapter 1.To answer the question we discuss how to handle time series, specially how to extract information from time series and what kind of novelties can be present in time series. Also unportant are measures for the quality of a novelty detection system. These topics are discussed in chapter 2.Another necessary ingredient for answering the question are implementations of the two approaches. The Kohonen self—organizing feature map is chosen as the most appropriate implementation for novelty detection based on clustering static information of a time series system, while a Tapped Delay Line Multilayer Perceptron is used for novelty detection based on time series prediction using dynamic information. The background of the used neural network architectures and experimental results of these two neural network methods are discussed in chapters 3 and 4. The results of the experiments and some of the discussed quality measures are used to give the final answer to the hypothesis stated in chapter 1. This answer is presented in chapter 5 together with recommendations for further research.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:29
Last Modified: 15 Feb 2018 07:29

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