الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The prevalent issue of the thesis is to get a new time series classification
method to classify (or allocate) an observed time series {Xt, t = 1, 2, …, N} to one
of the k populations (or categories) 1, 2,..., k with minimal error margin. Time
series classification has drawn a lot of attention in literature. Thus, a method of
classification using frequency domain is implemented to evaluate the linear and
quadratic functions. Further, the classification by motifs, using SAX
approximation, is used as well to give better accuracy than adopting other methods
of classification; concerning calculations, a number of packages and programming
languages (R 3.5.2, Python, Matlab 2018, Minitab 17) are used. The scope of the
thesis is then roughly as follows.