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

By Yang Lan


Time series data mining is one branch of data mining. Time series analysis\ud and prediction have always played an important role in human activities and\ud natural sciences. A Pseudo-Periodical time series has a complex structure,\ud with fluctuations and frequencies of the times series changing over time. Currently,\ud Pseudo-Periodicity of time series brings new properties and challenges\ud to time series analysis and prediction.\ud This thesis proposes two original computational approaches for time series\ud analysis and prediction: Moving Average of nth-order Difference (MANoD)\ud and Series Features Extraction (SFE). Based on data-driven methods, the\ud two original approaches open new insights in time series analysis and prediction\ud contributing with new feature detection techniques. The proposed\ud algorithms can reveal hidden patterns based on the characteristics of time\ud series, and they can be applied for predicting forthcoming events.\ud This thesis also presents the evaluation results of proposed algorithms on\ud various pseudo-periodical time series, and compares the predicting results\ud with classical time series prediction methods. The results of the original\ud approaches applied to real world and synthetic time series are very good and\ud show that the contributions open promising research directions

Topics: Time series, Time series analysis and prediction, nth-order difference, Similarity, Feature extraction, Data mining
Publisher: School of Computing, Informatics & Media
Year: 2009
OAI identifier:
Provided by: Bradford Scholars

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