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Computational Approaches for Time Series Analysis and Prediction. Data-Driven Methods for Pseudo-Periodical Sequences.
Lan, Yang
Lan, Yang
Publication Date
2010-05-28T15:35:25Z
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The University of Bradford theses are licenced under a Creative Commons Licence.
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Accepted for publication
Institution
University of Bradford
Department
School of Computing, Informatics & Media
Awarded
2009
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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.
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Type
Thesis
Qualification name
PhD