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

Description

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.

Links & Downloads

1. http://hdl.handle.net/10454/4317

Tags

Time series

Time series analysis and prediction

nth-order difference

Similarity

Feature extraction

Data mining

Additional Fields

Identifer	oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/4317
Date	January 2009
Creators	Lan, Yang
Contributors	Neagu, Daniel
Publisher	University of Bradford, School of Computing, Informatics & Media
Source Sets	Bradford Scholars
Language	English
Detected Language	English
Type	Thesis, doctoral, PhD
Rights	<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.