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    Computational Approaches for Time Series Analysis and Prediction. Data-Driven Methods for Pseudo-Periodical Sequences.

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    Publication date
    2010-05-28T15:35:25Z
    Author
    Lan, Yang
    Supervisor
    Neagu, Daniel
    Keyword
    Time series
    Time series analysis and prediction
    nth-order difference
    Similarity
    Feature extraction
    Data mining
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    School of Computing, Informatics & Media
    Awarded
    2009
    
    Metadata
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    Abstract
    Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. Currently, Pseudo-Periodicity of time series brings new properties and challenges to time series analysis and prediction. This thesis proposes two original computational approaches for time series analysis and prediction: Moving Average of nth-order Difference (MANoD) and Series Features Extraction (SFE). Based on data-driven methods, the two original approaches open new insights in time series analysis and prediction contributing with new feature detection techniques. The proposed algorithms can reveal hidden patterns based on the characteristics of time series, and they can be applied for predicting forthcoming events. This thesis also presents the evaluation results of proposed algorithms on various pseudo-periodical time series, and compares the predicting results with classical time series prediction methods. The results of the original approaches applied to real world and synthetic time series are very good and show that the contributions open promising research directions.
    URI
    http://hdl.handle.net/10454/4317
    Type
    Thesis
    Qualification name
    PhD
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    Theses

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