The Influence of Global Constraints on Similarity Measures for Time-Series Databases

A time series consists of a series of values or events obtained over repeated measurements in time. Analysis of time series represents and important tool in many application areas, such as stock market analysis, process and quality control, observation of natural phenomena, medical treatments, etc. A vital component in many types of time-series analysis is the choice of an appropriate distance/similarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. Being of quadratic time complexity, however, global constraints are often employed to limit the search space in the matrix during the dynamic programming procedure, in order to speed up computation. Furthermore, it has been reported that such constrained measures can also achieve better accuracy. In this paper, we investigate two representative time-series distance/similarity measures based on dynamic programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), and the effects of global constraints on them. Through extensive experiments on a large number of time-series data sets, we demonstrate how global constrains can significantly reduce the computation time of DTW and LCS. We also show that, if the constraint parameter is tight enough (less than 10-15% of time-series length), the constrained measure becomes significantly different from its unconstrained counterpart, in the sense of producing qualitatively different 1-nearest neighbor graphs. This observation explains the potential for accuracy gains when using constrained measures, highlighting the need for careful tuning of constraint parameters in order to achieve a good trade-off between speed and accuracy

A Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time Warping using Kalman Filter and Dynamic State Model

Dynamic speech properties such as time warping, silence removal and background noise interference are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. An adaptive framing based continuous speech tracking and similarity measurement approach is introduced in this work following a comprehensive research conducted in the diverse areas of speech processing. A dynamic state model is introduced based on system of linear motion equations which models the input (test) speech signal frame as a unidirectional moving object along the template speech signal. The most similar corresponding frame position in the template speech is estimated which is fused with a feature based similarity observation and the noise variances using a Kalman filter. The Kalman filter provides the final estimated frame position in the template speech at current time which is further used for prediction of a new frame size for the next step. In addition, a keyword spotting approach is proposed by introducing wavelet decomposition based dynamic noise filter and combination of beliefs. The Dempster’s theory of belief combination is deployed for the first time in relation to keyword spotting task. Performances for both; speech tracking and keyword spotting approaches are evaluated using the statistical metrics and gold standards for the binary classification. Experimental results proved the superiority of the proposed approaches over the existing methods.The appendices files are not available online

Dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users

[EN] The electricity sector is currently undergoing a process of liberalization and separation of roles, which is being implemented under the regulatory auspices of each Member State of the European Union and, therefore, with different speeds, perspectives and objectives that must converge on a common horizon, where Europe will benefit from an interconnected energy market in which producers and consumers can participate in free competition. This process of liberalization and separation of roles involves two consequences or, viewed another way, entails a major consequence from which other immediate consequence, as a necessity, is derived. The main consequence is the increased complexity in the management and supervision of a system, the electrical, increasingly interconnected and participatory, with connection of distributed energy sources, much of them from renewable sources, at different voltage levels and with different generation capacity at any point in the network. From this situation the other consequence is derived, which is the need to communicate information between agents, reliably, safely and quickly, and that this information is analyzed in the most effective way possible, to form part of the processes of decision taking that improve the observability and controllability of a system which is increasing in complexity and number of agents involved. With the evolution of Information and Communication Technologies (ICT), and the investments both in improving existing measurement and communications infrastructure, and taking the measurement and actuation capacity to a greater number of points in medium and low voltage networks, the availability of data that informs of the state of the network is increasingly higher and more complete. All these systems are part of the so-called Smart Grids, or intelligent networks of the future, a future which is not so far. One such source of information comes from the energy consumption of customers, measured on a regular basis (every hour, half hour or quarter-hour) and sent to the Distribution System Operators from the Smart Meters making use of Advanced Metering Infrastructure (AMI). This way, there is an increasingly amount of information on the energy consumption of customers, being stored in Big Data systems. This growing source of information demands specialized techniques which can take benefit from it, extracting a useful and summarized knowledge from it. This thesis deals with the use of this information of energy consumption from Smart Meters, in particular on the application of data mining techniques to obtain temporal patterns that characterize the users of electrical energy, grouping them according to these patterns in a small number of groups or clusters, that allow evaluating how users consume energy, both during the day and during a sequence of days, allowing to assess trends and predict future scenarios. For this, the current techniques are studied and, proving that the current works do not cover this objective, clustering or dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users are developed. These techniques are tested and validated on a database of hourly energy consumption values for a sample of residential customers in Spain during years 2008 and 2009. The results allow to observe both the characterization in consumption patterns of the different types of residential energy consumers, and their evolution over time, and to assess, for example, how the regulatory changes that occurred in Spain in the electricity sector during those years influenced in the temporal patterns of energy consumption.[ES] El sector eléctrico se halla actualmente sometido a un proceso de liberalización y separación de roles, que está siendo aplicado bajo los auspicios regulatorios de cada Estado Miembro de la Unión Europea y, por tanto, con distintas velocidades, perspectivas y objetivos que deben confluir en un horizonte común, en donde Europa se beneficiará de un mercado energético interconectado, en el cual productores y consumidores podrán participar en libre competencia. Este proceso de liberalización y separación de roles conlleva dos consecuencias o, visto de otra manera, conlleva una consecuencia principal de la cual se deriva, como necesidad, otra consecuencia inmediata. La consecuencia principal es el aumento de la complejidad en la gestión y supervisión de un sistema, el eléctrico, cada vez más interconectado y participativo, con conexión de fuentes distribuidas de energía, muchas de ellas de origen renovable, a distintos niveles de tensión y con distinta capacidad de generación, en cualquier punto de la red. De esta situación se deriva la otra consecuencia, que es la necesidad de comunicar información entre los distintos agentes, de forma fiable, segura y rápida, y que esta información sea analizada de la forma más eficaz posible, para que forme parte de los procesos de toma de decisiones que mejoran la observabilidad y controlabilidad de un sistema cada vez más complejo y con más agentes involucrados. Con el avance de las Tecnologías de Información y Comunicaciones (TIC), y las inversiones tanto en mejora de la infraestructura existente de medida y comunicaciones, como en llevar la obtención de medidas y la capacidad de actuación a un mayor número de puntos en redes de media y baja tensión, la disponibilidad de datos sobre el estado de la red es cada vez mayor y más completa. Todos estos sistemas forman parte de las llamadas Smart Grids, o redes inteligentes del futuro, un futuro ya no tan lejano. Una de estas fuentes de información proviene de los consumos energéticos de los clientes, medidos de forma periódica (cada hora, media hora o cuarto de hora) y enviados hacia las Distribuidoras desde los contadores inteligentes o Smart Meters, mediante infraestructura avanzada de medida o Advanced Metering Infrastructure (AMI). De esta forma, cada vez se tiene una mayor cantidad de información sobre los consumos energéticos de los clientes, almacenada en sistemas de Big Data. Esta cada vez mayor fuente de información demanda técnicas especializadas que sepan aprovecharla, extrayendo un conocimiento útil y resumido de la misma. La presente Tesis doctoral versa sobre el uso de esta información de consumos energéticos de los contadores inteligentes, en concreto sobre la aplicación de técnicas de minería de datos (data mining) para obtener patrones temporales que caractericen a los usuarios de energía eléctrica, agrupándolos según estos mismos patrones en un número reducido de grupos o clusters, que permiten evaluar la forma en que los usuarios consumen la energía, tanto a lo largo del día como durante una secuencia de días, permitiendo evaluar tendencias y predecir escenarios futuros. Para ello se estudian las técnicas actuales y, comprobando que los trabajos actuales no cubren este objetivo, se desarrollan técnicas de clustering o segmentación dinámica aplicadas a curvas de carga de consumo eléctrico diario de clientes domésticos. Estas técnicas se prueban y validan sobre una base de datos de consumos energéticos horarios de una muestra de clientes residenciales en España durante los años 2008 y 2009. Los resultados permiten observar tanto la caracterización en consumos de los distintos tipos de consumidores energéticos residenciales, como su evolución en el tiempo, y permiten evaluar, por ejemplo, cómo influenciaron en los patrones temporales de consumos los cambios regulatorios que se produjeron en España en el sector eléctrico durante esos años.[CA] El sector elèctric es troba actualment sotmès a un procés de liberalització i separació de rols, que s'està aplicant davall els auspicis reguladors de cada estat membre de la Unió Europea i, per tant, amb distintes velocitats, perspectives i objectius que han de confluir en un horitzó comú, on Europa es beneficiarà d'un mercat energètic interconnectat, en el qual productors i consumidors podran participar en lliure competència. Aquest procés de liberalització i separació de rols comporta dues conseqüències o, vist d'una altra manera, comporta una conseqüència principal de la qual es deriva, com a necessitat, una altra conseqüència immediata. La conseqüència principal és l'augment de la complexitat en la gestió i supervisió d'un sistema, l'elèctric, cada vegada més interconnectat i participatiu, amb connexió de fonts distribuïdes d'energia, moltes d'aquestes d'origen renovable, a distints nivells de tensió i amb distinta capacitat de generació, en qualsevol punt de la xarxa. D'aquesta situació es deriva l'altra conseqüència, que és la necessitat de comunicar informació entre els distints agents, de forma fiable, segura i ràpida, i que aquesta informació siga analitzada de la manera més eficaç possible, perquè forme part dels processos de presa de decisions que milloren l'observabilitat i controlabilitat d'un sistema cada vegada més complex i amb més agents involucrats. Amb l'avanç de les tecnologies de la informació i les comunicacions (TIC), i les inversions, tant en la millora de la infraestructura existent de mesura i comunicacions, com en el trasllat de l'obtenció de mesures i capacitat d'actuació a un nombre més gran de punts en xarxes de mitjana i baixa tensió, la disponibilitat de dades sobre l'estat de la xarxa és cada vegada major i més completa. Tots aquests sistemes formen part de les denominades Smart Grids o xarxes intel·ligents del futur, un futur ja no tan llunyà. Una d'aquestes fonts d'informació prové dels consums energètics dels clients, mesurats de forma periòdica (cada hora, mitja hora o quart d'hora) i enviats cap a les distribuïdores des dels comptadors intel·ligents o Smart Meters, per mitjà d'infraestructura avançada de mesura o Advanced Metering Infrastructure (AMI). D'aquesta manera, cada vegada es té una major quantitat d'informació sobre els consums energètics dels clients, emmagatzemada en sistemes de Big Data. Aquesta cada vegada major font d'informació demanda tècniques especialitzades que sàpiguen aprofitar-la, extraient-ne un coneixement útil i resumit. La present tesi doctoral versa sobre l'ús d'aquesta informació de consums energètics dels comptadors intel·ligents, en concret sobre l'aplicació de tècniques de mineria de dades (data mining) per a obtenir patrons temporals que caracteritzen els usuaris d'energia elèctrica, agrupant-los segons aquests mateixos patrons en una quantitat reduïda de grups o clusters, que permeten avaluar la forma en què els usuaris consumeixen l'energia, tant al llarg del dia com durant una seqüència de dies, i que permetent avaluar tendències i predir escenaris futurs. Amb aquesta finalitat, s'estudien les tècniques actuals i, en comprovar que els treballs actuals no cobreixen aquest objectiu, es desenvolupen tècniques de clustering o segmentació dinàmica aplicades a corbes de càrrega de consum elèctric diari de clients domèstics. Aquestes tècniques es proven i validen sobre una base de dades de consums energètics horaris d'una mostra de clients residencials a Espanya durant els anys 2008 i 2009. Els resultats permeten observar tant la caracterització en consums dels distints tipus de consumidors energètics residencials, com la seua evolució en el temps, i permeten avaluar, per exemple, com van influenciar en els patrons temporals de consums els canvis reguladors que es van produir a Espanya en el sector elèctric durant aquests anys.Benítez Sánchez, IJ. (2015). Dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59236TESI

Fast, Scalable, and Accurate Algorithms for Time-Series Analysis

Time is a critical element for the understanding of natural processes (e.g., earthquakes and weather) or human-made artifacts (e.g., stock market and speech signals). The analysis of time series, the result of sequentially collecting observations of such processes and artifacts, is becoming increasingly prevalent across scientific and industrial applications. The extraction of non-trivial features (e.g., patterns, correlations, and trends) in time series is a critical step for devising effective time-series mining methods for real-world problems and the subject of active research for decades. In this dissertation, we address this fundamental problem by studying and presenting computational methods for efficient unsupervised learning of robust feature representations from time series. Our objective is to (i) simplify and unify the design of scalable and accurate time-series mining algorithms; and (ii) provide a set of readily available tools for effective time-series analysis. We focus on applications operating solely over time-series collections and on applications where the analysis of time series complements the analysis of other types of data, such as text and graphs. For applications operating solely over time-series collections, we propose a generic computational framework, GRAIL, to learn low-dimensional representations that natively preserve the invariances offered by a given time-series comparison method. GRAIL represents a departure from classic approaches in the time-series literature where representation methods are agnostic to the similarity function used in subsequent learning processes. GRAIL relies on the attractive idea that once we construct the data-to-data similarity matrix most time-series mining tasks can be trivially solved. To overcome scalability issues associated with approaches relying on such matrices, GRAIL exploits time-series clustering to construct a small set of landmark time series and learns representations to reduce the data-to-data matrix to a data-to-landmark points matrix. To demonstrate the effectiveness of GRAIL, we first present domain-independent, highly accurate, and scalable time-series clustering methods to facilitate exploration and summarization of time-series collections. Then, we show that GRAIL representations, when combined with suitable methods, significantly outperform, in terms of efficiency and accuracy, state-of-the-art methods in major time-series mining tasks, such as querying, clustering, classification, sampling, and visualization. Overall, GRAIL rises as a new primitive for highly accurate, yet scalable, time-series analysis. For applications where the analysis of time series complements the analysis of other types of data, such as text and graphs, we propose generic, simple, and lightweight methodologies to learn features from time-varying measurements. Such applications often organize operations over different types of data in a pipeline such that one operation provides input---in the form of feature vectors---to subsequent operations. To reason about the temporal patterns and trends in the underlying features, we need to (i) track the evolution of features over different time periods; and (ii) transform these time-varying features into actionable knowledge (e.g., forecasting an outcome). To address this challenging problem, we propose principled approaches to model time-varying features and study two large-scale, real-world, applications. Specifically, we first study the problem of predicting the impact of scientific concepts through temporal analysis of characteristics extracted from the metadata and full text of scientific articles. Then, we explore the promise of harnessing temporal patterns in behavioral signals extracted from web search engine logs for early detection of devastating diseases. In both applications, combinations of features with time-series relevant features yielded the greatest impact than any other indicator considered in our analysis. We believe that our simple methodology, along with the interesting domain-specific findings that our work revealed, will motivate new studies across different scientific and industrial settings

Proof of Concept For the Use of Motion Capture Technology In Athletic Pedagogy

Visualization has long been an important method for conveying complex information. Where information transfer using written and spoken means might amount to 200-250 words per minute, visual media can often convey information at many times this rate. This makes visualization a potentially important tool for education. Athletic instruction, particularly, can involve communication about complex human movement that is not easily conveyed with written or spoken descriptions. Video based instruction can be problematic since video data can contain too much information, thereby making it more difficult for a student to absorb what is cognitively necessary. The lesson is to present the learner what is needed and not more. We present a novel use of motion capture animation as an educational tool for teaching athletic movements. The advantage of motion capture is its ability to accurately represent real human motion in a minimalist context which removes extraneous information normally found in video. Motion capture animation only displays motion information, not additional information regarding the motion context. Producing an “automated coach” would be too large and difficult a problem to solve within the scope of a Master's thesis but we can perform initial steps including producing a useful software tool which performs data analysis on two motion datasets. We believe such a tool would be beneficial to a human coach as an analysis tool and the work would provide some useful understanding of next important steps towards perhaps someday producing an automated coach

Spatio-Temporal Video Analysis and the 3D Shearlet Transform

Abstract The automatic analysis of the content of a video sequence has captured the attention of the computer vision community for a very long time. Indeed, video understanding, which needs to incorporate both semantic and dynamic cues, may be trivial for humans, but it turned out to be a very complex task for a machine. Over the years the signal processing, computer vision, and machine learning communities contributed with algorithms that are today effective building blocks of more and more complex systems. In the meanwhile, theoretical analysis has gained a better understanding of this multifaceted type of data. Indeed, video sequences are not only high dimensional data, but they are also very peculiar, as they include spatial as well as temporal information which should be treated differently, but are both important to the overall process. The work of this thesis builds a new bridge between signal processing theory, and computer vision applications. It considers a novel approach to multi resolution signal processing, the so-called Shearlet Transform, as a reference framework for representing meaningful space-time local information in a video signal. The Shearlet Transform has been shown effective in analyzing multi-dimensional signals, ranging from images to x-ray tomographic data. As a tool for signal denoising, has also been applied to video data. However, to the best of our knowledge, the Shearlet Transform has never been employed to design video analysis algorithms. In this thesis, our broad objective is to explore the capabilities of the Shearlet Transform to extract information from 2D+T-dimensional data. We exploit the properties of the Shearlet decomposition to redesign a variety of classical video processing techniques (including space-time interest point detection and normal flow estimation) and to develop novel methods to better understand the local behavior of video sequences. We provide experimental evidence on the potential of our approach on synthetic as well as real data drawn from publicly available benchmark datasets. The results we obtain show the potential of our approach and encourages further investigations in the near future

Deep Clustering and Deep Network Compression

The use of deep learning has grown increasingly in recent years, thereby becoming a much-discussed topic across a diverse range of fields, especially in computer vision, text mining, and speech recognition. Deep learning methods have proven to be robust in representation learning and attained extraordinary achievement. Their success is primarily due to the ability of deep learning to discover and automatically learn feature representations by mapping input data into abstract and composite representations in a latent space. Deep learning’s ability to deal with high-level representations from data has inspired us to make use of learned representations, aiming to enhance unsupervised clustering and evaluate the characteristic strength of internal representations to compress and accelerate deep neural networks.Traditional clustering algorithms attain a limited performance as the dimensionality in-creases. Therefore, the ability to extract high-level representations provides beneficial components that can support such clustering algorithms. In this work, we first present DeepCluster, a clustering approach embedded in a deep convolutional auto-encoder. We introduce two clustering methods, namely DCAE-Kmeans and DCAE-GMM. The DeepCluster allows for data points to be grouped into their identical cluster, in the latent space, in a joint-cost function by simultaneously optimizing the clustering objective and the DCAE objective, producing stable representations, which is appropriate for the clustering process. Both qualitative and quantitative evaluations of proposed methods are reported, showing the efficiency of deep clustering on several public datasets in comparison to the previous state-of-the-art methods.Following this, we propose a new version of the DeepCluster model to include varying degrees of discriminative power. This introduces a mechanism which enables the imposition of regularization techniques and the involvement of a supervision component. The key idea of our approach is to distinguish the discriminatory power of numerous structures when searching for a compact structure to form robust clusters. The effectiveness of injecting various levels of discriminatory powers into the learning process is investigated alongside the exploration and analytical study of the discriminatory power obtained through the use of two discriminative attributes: data-driven discriminative attributes with the support of regularization techniques, and supervision discriminative attributes with the support of the supervision component. An evaluation is provided on four different datasets.The use of neural networks in various applications is accompanied by a dramatic increase in computational costs and memory requirements. Making use of the characteristic strength of learned representations, we propose an iterative pruning method that simultaneously identifies the critical neurons and prunes the model during training without involving any pre-training or fine-tuning procedures. We introduce a majority voting technique to compare the activation values among neurons and assign a voting score to evaluate their importance quantitatively. This mechanism effectively reduces model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Empirically, we demonstrate that our pruning method is robust across various scenarios, including fully-connected networks (FCNs), sparsely-connected networks (SCNs), and Convolutional neural networks (CNNs), using two public datasets.Moreover, we also propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts, with the aim of evaluating the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a se-mantic concept and individual hidden unit representations is utilized to evaluate feature maps’ importance quantitatively. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines.To conclude, we present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a founding contribution to the area of applying deep clustering to time-series data by presenting the first case study in the context of movement behavior clustering utilizing the DeepCluster method. The results are promising, showing that the latent space encodes sufficient patterns to facilitate accurate clustering of movement behaviors. Finally, we identify state-of-the-art and present an outlook on this important field of DTSC from five important perspectives

Unsupervised video indexing on audiovisual characterization of persons

Cette thèse consiste à proposer une méthode de caractérisation non-supervisée des intervenants dans les documents audiovisuels, en exploitant des données liées à leur apparence physique et à leur voix. De manière générale, les méthodes d'identification automatique, que ce soit en vidéo ou en audio, nécessitent une quantité importante de connaissances a priori sur le contenu. Dans ce travail, le but est d'étudier les deux modes de façon corrélée et d'exploiter leur propriété respective de manière collaborative et robuste, afin de produire un résultat fiable aussi indépendant que possible de toute connaissance a priori. Plus particulièrement, nous avons étudié les caractéristiques du flux audio et nous avons proposé plusieurs méthodes pour la segmentation et le regroupement en locuteurs que nous avons évaluées dans le cadre d'une campagne d'évaluation. Ensuite, nous avons mené une étude approfondie sur les descripteurs visuels (visage, costume) qui nous ont servis à proposer de nouvelles approches pour la détection, le suivi et le regroupement des personnes. Enfin, le travail s'est focalisé sur la fusion des données audio et vidéo en proposant une approche basée sur le calcul d'une matrice de cooccurrence qui nous a permis d'établir une association entre l'index audio et l'index vidéo et d'effectuer leur correction. Nous pouvons ainsi produire un modèle audiovisuel dynamique des intervenants.This thesis consists to propose a method for an unsupervised characterization of persons within audiovisual documents, by exploring the data related for their physical appearance and their voice. From a general manner, the automatic recognition methods, either in video or audio, need a huge amount of a priori knowledge about their content. In this work, the goal is to study the two modes in a correlated way and to explore their properties in a collaborative and robust way, in order to produce a reliable result as independent as possible from any a priori knowledge. More particularly, we have studied the characteristics of the audio stream and we have proposed many methods for speaker segmentation and clustering and that we have evaluated in a french competition. Then, we have carried a deep study on visual descriptors (face, clothing) that helped us to propose novel approches for detecting, tracking, and clustering of people within the document. Finally, the work was focused on the audiovisual fusion by proposing a method based on computing the cooccurrence matrix that allowed us to establish an association between audio and video indexes, and to correct them. That will enable us to produce a dynamic audiovisual model for each speaker

Uloga mera sličnosti u analizi vremenskih serija

The subject of this dissertation encompasses a comprehensive overview and analysis of the impact of Sakoe-Chiba global constraint on the most commonly used elastic similarity measures in the field of time-series data mining with a focus on classification accuracy. The choice of similarity measure is one of the most significant aspects of time-series analysis  -  it should correctly reflect the resemblance between the data presented in the form of time series. Similarity measures represent a critical component of many tasks of mining time series, including: classification, clustering, prediction, anomaly detection, and others. The research covered by this dissertation is oriented on several issues: 1.  review of the effects of  global constraints on the performance of computing similarity measures, 2.  a detailed analysis of the influence of constraining the elastic similarity measures on the accuracy of classical classification techniques, 3.  an extensive study of the impact of different weighting schemes on the classification of time series, 4.  development of an open source library that integrates the main techniques and methods required for analysis and mining time series, and which is used for the realization of these experimentsPredmet istraživanja ove disertacije obuhvata detaljan pregled i analizu uticaja Sakoe-Chiba globalnog ograničenja na najčešće korišćene elastične mere sličnosti u oblasti data mining-a vremenskih serija sa naglaskom na tačnost klasifikacije. Izbor mere sličnosti jedan je od najvažnijih aspekata analize vremenskih serija  -  ona treba  verno reflektovati sličnost između podataka prikazanih u obliku vremenskih serija.  Mera sličnosti predstavlјa kritičnu komponentu mnogih zadataka  mining-a vremenskih serija, uklјučujući klasifikaciju, grupisanje (eng.  clustering), predviđanje, otkrivanje anomalija i drugih. Istraživanje obuhvaćeno ovom disertacijom usmereno je na nekoliko pravaca: 1.  pregled efekata globalnih ograničenja na performanse računanja mera sličnosti, 2.  detalјna analiza posledice ograničenja elastičnih mera sličnosti na tačnost klasifikacije klasičnih tehnika klasifikacije, 3.  opsežna studija uticaj različitih načina računanja težina (eng. weighting scheme) na klasifikaciju vremenskih serija, 4.  razvoj biblioteke otvorenog koda (Framework for Analysis and Prediction  -  FAP) koja će integrisati glavne tehnike i metode potrebne za analizu i mining  vremenskih serija i koja je korišćena za realizaciju ovih eksperimenata.Predmet istraživanja ove disertacije obuhvata detaljan pregled i analizu uticaja Sakoe-Chiba globalnog ograničenja na najčešće korišćene elastične mere sličnosti u oblasti data mining-a vremenskih serija sa naglaskom na tačnost klasifikacije. Izbor mere sličnosti jedan je od najvažnijih aspekata analize vremenskih serija  -  ona treba  verno reflektovati sličnost između podataka prikazanih u obliku vremenskih serija.  Mera sličnosti predstavlja kritičnu komponentu mnogih zadataka  mining-a vremenskih serija, uključujući klasifikaciju, grupisanje (eng.  clustering), predviđanje, otkrivanje anomalija i drugih. Istraživanje obuhvaćeno ovom disertacijom usmereno je na nekoliko pravaca: 1.  pregled efekata globalnih ograničenja na performanse računanja mera sličnosti, 2.  detaljna analiza posledice ograničenja elastičnih mera sličnosti na tačnost klasifikacije klasičnih tehnika klasifikacije, 3.  opsežna studija uticaj različitih načina računanja težina (eng. weighting scheme) na klasifikaciju vremenskih serija, 4.  razvoj biblioteke otvorenog koda (Framework for Analysis and Prediction  -  FAP) koja će integrisati glavne tehnike i metode potrebne za analizu i mining  vremenskih serija i koja je korišćena za realizaciju ovih eksperimenata