Optimization in semi-supervised classification of multivariate time series

Abstract. In this thesis, I study methods that classify time series in a semi-supervised manner. I compare the performance of models that assume independent and identically distributed observations against models that assume nearby observations to be dependent of each other. These models are evaluated on three real world time series data sets. In addition, I carefully go through the theory of mathematical optimization behind two successful algorithms used in this thesis: Support Vector Data Description and Dynamic Time Warping. For the algorithm Dynamic Time Warping, I provide a novel proof that is based on dynamic optimization. The experiments in this thesis suggest that the assumption of observations in time series to be independent and identically distributed may deteriorate the results of semi-supervised classification. The novel self-training method presented in this thesis called Peak Evaluation using Perceptually Important Points shows great performance on multivariate time series compared to the methods currently existing in literature. The feature subset selection of multivariate time series may improve classification performance, but finding a reliable unsupervised feature subset selection method remains an open question

An automated signal alignment algorithm based on dynamic time warping for capillary electrophoresis data

Correcting the retention time variation and measuring the similarity of time series is one of the most popular challenges in the area of analyzing capillary electrophoresis (CE) data. In this study, an automated signal alignment method is proposed by modifying the dynamic time warping (DTW) approach to align the time-series data. Preprocessing tools and further optimizations were developed to increase the performance of the algorithm. As a demonstrative case study, the developed algorithm is applied to the analysis of CE data from a selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE) evaluation of the RNA secondary structure. The time-shift problem is one of the main components in the analysis of the SHAPE data. The accuracy and execution time of the algorithm are illustrated with experimental results obtained by applying to different types of data. The experimental results show that the signal alignment algorithm efficiently corrects the retention time variation. The developed tools can be readily adapted for the analysis of other biological datasets or time series

Analiza i predviđanje toka vremenskih serija pomoću “Case-BasedReasoning” tehnologije.

This thesis describes one promising approach where a problem of time series analysis and prediction was solved by using Case Based Reasoning (CBR) technology. Foundations and main concepts of this technology are described in detail. Furthermore, a detailed study of different approaches in time series analysis is given. System CuBaGe (Curve Base Generator) - A robust and general architecture for curve representation and indexing time series databases, based on Case based reasoning technology, was developed. Also, a corresponding similarity measure was modelled for a given kind of curve representation. The presented architecture may be employed equally well not only in conventional time series (where all values are known), but also in some non-standard time series (sparse, vague, non-equidistant). Dealing with the non-standard time series is the highest advantage of the presented architecture.U ovoj doktorskoj disertaciji prikazan je interesantan i perspektivan pristup rešavanja problema analize i predviđanja vremenskih serija korišćenjem Case Based Reasoning (CBR) tehnologije. Detaljno su opisane osnove i glavni koncepti ove tehnologije. Takođe, data je komparativna analiza različitih pristupa u analizi vremenskih serija sa posebnim osvrtom na predviđanje. Kao najveći doprinos ove disertacije, prikazan je sistem CuBaGe (Curve Base Generator) u kome je realizovan originalni način reprezentacije vremenskih serija zajedno sa, takođe originalnom, odgovarajućom merom sličnosti. Robusnost i generalnost sistema ilustrovana je realnom primenom u domenu finansijskog predviđanja, gde je pokazano da sistem jednako dobro funkcioniše sa standardnim, ali i sa nekim nestandardnim vremenskim serijama (neodređenim, retkim i neekvidistantnim)