Author: Avraam Tapinos

Publisher:

Published: 2013

Total Pages:

ISBN-13:

Analysis of time series data constitutes an important activity in many scientific disciplines. Over the last years there has been an increase in the collection of time series data in all scientific fields and disciplines, such as the industry and engineering. Due to the increasing size of the time series datasets, new automated time series data mining techniques have been devised for comparing time series data and present information in a logical and easily comprehensible structure. In systems biology in particular, time series are used to the study biological systems. The time series representations of a systems' dynamics behaviour are multivariate time series. Time series are considered multivariate when they contain observations for more than one variable component. The biological systems' dynamics time series contain observations for every feature component that is included in the system; they thus are multivariate time series. Recently, there has been an increasing interest in the collection of biological time series. It would therefore be beneficial for systems biologist to be able to compare these multivariate time series. Over the last decade, the field of time series analysis has attracted the attention of people from different scientific disciplines. A number of researchers from the data mining community focus their efforts on providing solutions on numerous problems regarding different time series data mining tasks. Different methods have been proposed for instance, for comparing, indexing and clustering, of univariate time series. Furthermore, different methods have been proposed for creating abstract representations of time series data and investigating the benefits of using these representations for data mining tasks. The introduction of more advanced computing resources facilitated the collection of multivariate time series, which has become common practise in various scientific fields. The increasing number of multivariate time series data triggered the demand for methods to compare them. A small number of well-suited methods have been proposed for comparing these multivariate time series data. All the currently available methods for multivariate time series comparison are more than adequate for comparing multivariate time series with the same dimensionality. However, they all suffer the same drawback. Current techniques cannot process multivariate time series with different dimensions. A proposed solution for comparing multivariate time series with arbitrary dimensions requires the creation of weighted averages. However, the accumulation of weights data is not always feasible. In this project, a new method is proposed which enables the comparison of multivariate time series with arbitrary dimensions. The particular method is evaluated on multivariate time series from different disciplines in order to test the methods' applicability on data from different fields of science and industry. Lastly, the newly formed method is applied to perform different time series data mining analyses on a set of biological data.