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Relevant bibliographies by topics / Time series data management / Dissertations / Theses

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Dissertations / Theses on the topic 'Time series data management'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Matus, Castillejos Abel, and n/a. "Management of Time Series Data." University of Canberra. Information Sciences & Engineering, 2006. http://erl.canberra.edu.au./public/adt-AUC20070111.095300.

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Every day large volumes of data are collected in the form of time series. Time series are collections of events or observations, predominantly numeric in nature, sequentially recorded on a regular or irregular time basis. Time series are becoming increasingly important in nearly every organisation and industry, including banking, finance, telecommunication, and transportation. Banking institutions, for instance, rely on the analysis of time series for forecasting economic indices, elaborating financial market models, and registering international trade operations. More and more time series are

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2

Siwela, Blessing. "Web-based management of time-series raster data." Master's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/6441.

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Data discovery and data handling often presents serious challenges to organizations that manage huge archives of raster datasets such as those generated by satellite remote sensing. Satellite remote sensing produces a regular stream of raster datasets used in many applications including environmental and agricultural monitoring. This thesis presents a system architecture for the management of time-series GIS raster datasets. The architecture is then applied in a prototype implementation for a department that uses remote sensing data for agricultural monitoring. The architecture centres on thre

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Mousavi, Bamdad. "Scalable Stream Processing and Management for Time Series Data." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42295.

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There has been an enormous growth in the generation of time series data in the past decade. This trend is caused by widespread adoption of IoT technologies, the data generated by monitoring of cloud computing resources, and cyber physical systems. Although time series data have been a topic of discussion in the domain of data management for several decades, this recent growth has brought the topic to the forefront. Many of the time series management systems available today lack the necessary features to successfully manage and process the sheer amount of time series being generated today. In t

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Romanazzi, Stefano. "Water Supply Network Management: Sensor Analysis using Google Cloud Dataflow." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The growing field of IoT increases the amount of time series data produced every day. With such information overload it is necessary to promptly clean and process those information extracting meaningful knowledge and avoiding raw data storage. Nowadays cloud infrastructures allow to adopt this processing demand by providing new models for defining data-parallel processing pipelines, such as the Apache Beam unified model which evolved from Google Cloud Dataflow and MapReduce paradigm. The projects of this thesis have been implemented during a three-month internship at Injenia srl, and face th

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5

Alvidrez, Carlos. "A systematic framework for preparing and enhancing structured data sets for time series analysis." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100367.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 216-217).<br>This thesis proposes a framework to systematically prepare and enhance structured data for time series analysis. It suggests the production of intermediate derived calculations, which aid in the analysis and rationalization of variation over time, to enhance the consistency and the efficiency of data analysis. This thesis was developed with

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6

Battaglia, Bruno. "Studio e valutazione di database management system per la gestione di serie temporali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17270/.

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La tesi è incentrata sulle time series e la loro gestione. Dopo aver esposto cosa fosse una serie temporale ed alcuni casi di utilizzo, la dissertazione prosegue elencando le famiglie di DBMS ed i criteri attraverso i quali valutarli. Successivamente si è descritto il modello che ogni DBMS implementava e, dopo aver dato un accenno di esso, si è passati alle tecniche usate per la gestione e l'analisi delle serie temporali. Ancora dopo, invece, si sono viste le tecniche di modellazione di un database in grado di gestire serie storiche e sono stati analizzati tutti i DBMS presi in esame attravers

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Gogolou, Anna. "Iterative and Expressive Querying for Big Data Series." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS415.

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Les séries temporelles deviennent omniprésentes dans la vie moderne et leur analyse de plus en plus difficile compte tenu de leur taille. L’analyse des grandes séries de données implique des tâches telles que l’appariement de modèles (motifs), la détection d’anomalies, l’identification de modèles fréquents, et la classification ou le regroupement (clustering). Ces tâches reposent sur la notion de similarité. La communauté scientifique a proposé de plusieurs techniques, y compris de nombreuses mesures de similarité pour calculer la distance entre deux séries temporelles, ainsi que des technique

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8

Waitayangkoon, Chalermpol. "Factors Affecting the Efficient Performance of the Thai State Railway Authority: a Time-Series Data Analysis." Thesis, University of North Texas, 1988. https://digital.library.unt.edu/ark:/67531/metadc330635/.

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The Thai State Railway Authority (RSR) is a public enterprise in Thailand. As an organization its performance is subject to the argument of contingency theorists that operating efficiency is dependent upon various factors both in the internal and external environments of the enterprise. Most of the internal factors are those that organization theorists in the developed world have identified such as goals and objectives, resources, and organization structures. Meanwhile, external factors such as political, economic and social conditions of the society are regarded as indirect factors that have

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9

Winn, David. "An analysis of neural networks and time series techniques for demand forecasting." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004362.

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This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasti

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10

Jin, Chao. "Methodology on Exact Extraction of Time Series Features for Robust Prognostics and Health Monitoring." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504795992214385.

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11

Li, Yang. "The time-series approaches in forecasting one-step-ahead cash-flow data of mining companies listed on the Johannesburg Stock Exchange." Thesis, University of the Western Cape, 2007. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_1552_1254470577.

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<p>Previous research pertaining to the financial aspect of the mining industry has focused predominantly on mining products' values and the companies' sensitivity to exchange rates. There has been very little empirical research carries out in the field of the statistical behaviour of mning companies' cash flow data. This paper aimed to study the time-series behaviour of the cash flow data series of JSE listed mining companies.</p>

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12

Zeng, Chunqiu. "Large Scale Data Mining for IT Service Management." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/3051.

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More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure. Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automat

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13

Muhammad, Fuad Muhammad Marwan. "Similarity Search in High-dimensional Spaces with Applications to Time Series Data Mining and Information Retrieval." Phd thesis, Université de Bretagne Sud, 2011. http://tel.archives-ouvertes.fr/tel-00619953.

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Nous présentons l'un des principaux problèmes dans la recherche d'informations et de data mining, ce qui est le problème de recherche de similarité. Nous abordons ce problème dans une perspective essentiellement métrique. Nous nous concentrons sur des données de séries temporelles, mais notre objectif général est de développer des méthodes et des algorithmes qui peuvent être étendus aux autres types de données. Nous étudions de nouvelles méthodes pour traiter le problème de recherche de similarité dans des espaces haut-dimensionnels. Les nouvelles méthodes et algorithmes que nous introduisons

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Dalderop, Jeroen Wilhelmus Paulus. "Essays on nonparametric estimation of asset pricing models." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277966.

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This thesis studies the use of nonparametric econometric methods to reconcile the empirical behaviour of financial asset prices with theoretical valuation models. The confrontation of economic theory with asset price data requires various functional form assumptions about the preferences and beliefs of investors. Nonparametric methods provide a flexible class of models that can prevent misspecification of agents’ utility functions or the distribution of asset returns. Evidence for potential nonlinearity is seen in the presence of non-Gaussian distributions and excessive volatility of stock ret

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Vander, Elst Harry-Paul. "Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/228960.

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This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexi

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Kilborn, Joshua Paul. "Investigating Marine Resources in the Gulf of Mexico at Multiple Spatial and Temporal Scales of Inquiry." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7046.

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The work in this dissertation represents an attempt to investigate multiple temporal and spatial scales of inquiry relating to the variability of marine resources throughout the Gulf of Mexico large marine ecosystem (Gulf LME). This effort was undertaken over two spatial extents within the greater Gulf LME using two different time-series of fisheries monitoring data. Case studies demonstrating simple frameworks and best practices are presented with the aim of aiding researchers seeking to reduce errors and biases in scientific decision making. Two of the studies focused on

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Elmäng, Niclas. "Sequence classification on gamified behavior data from a learning management system : Predicting student outcome using neural networks and Markov chain." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18654.

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This study has investigated whether it is possible to classify time series data originating from a gamified learning management system. By using the school data provided by the gamification company Insert Coin AB, the aim was to distribute the teacher’s supervision more efficiently among students who are more likely to fail. Motivating this is the possibility that the student retention and completion rate can be increased. This was done by using Long short-term memory and convolutional neural networks and Markov chain to classify time series of event data. Since the classes are balanced the cl

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Mousheimish, Raef. "Combinaison de l’Internet des objets, du traitement d’évènements complexes et de la classification de séries temporelles pour une gestion proactive de processus métier." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV073/document.

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L’internet des objets est au coeur desprocessus industriels intelligents grâce à lacapacité de détection d’évènements à partir dedonnées de capteurs. Cependant, beaucoup resteà faire pour tirer le meilleur parti de cettetechnologie récente et la faire passer à l’échelle.Cette thèse vise à combler le gap entre les fluxmassifs de données collectées par les capteurs etleur exploitation effective dans la gestion desprocessus métier. Elle propose une approcheglobale qui combine le traitement de flux dedonnées, l’apprentissage supervisé et/oul’utilisation de règles sur des évènementscomplexes permet

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Ahsan, Ramoza. "Time Series Data Analytics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.

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Given the ubiquity of time series data, and the exponential growth of databases, there has recently been an explosion of interest in time series data mining. Finding similar trends and patterns among time series data is critical for many applications ranging from financial planning, weather forecasting, stock analysis to policy making. With time series being high-dimensional objects, detection of similar trends especially at the granularity of subsequences or among time series of different lengths and temporal misalignments incurs prohibitively high computation costs. Finding trend

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Fischer, Ulrike. "Forecasting in Database Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-133281.

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Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets.

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Chinipardaz, Rahim. "Discrimination of time series data." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.481472.

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VALENTIM, CAIO DIAS. "DATA STRUCTURES FOR TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21522@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Séries temporais são ferramentas importantes para análise de eventos que ocorrem em diferentes domínios do conhecimento humano, como medicina, física, meteorologia e finanças. Uma tarefa comum na análise de séries temporais é a busca por eventos pouco frequentes que refletem fatos de interesse sobre o domínio de origem da série. Neste trabalho, buscamos desenvolver técnicas para detecção de eventos raros em séries temporais. Formalmente, uma série temporal A igual a (a1, a2,..

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Matam, Basava R. "Watermarking biomedical time series data." Thesis, Aston University, 2009. http://publications.aston.ac.uk/15351/.

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This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidenc

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Mazel, David S. "Fractal modeling of time-series data." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/13916.

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Brunsdon, T. M. "Time series analysis of compositional data." Thesis, University of Southampton, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378257.

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Tang, Fengzhen. "Kernel methods for time series data." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5929/.

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Kernel methods are powerful learning techniques with excellent generalization capability. This thesis develops three advanced approaches within the generic SVM framework in the application domain of time series data. The first contribution presents a new methodology for incorporating privileged information about the future evolution of time series, which is only available in the training phase. The task is prediction of the ordered categories of future time series movements. This is implemented by directly extending support vector ordinal regression with implicit constraints to leaning using p

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Granberg, Patrick. "Churn prediction using time series data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294206.

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Customer churn is problematic for any business trying to expand their customer base. The acquisition of new customers to replace churned ones are associated with additional costs, whereas taking measures to retain existing customers may prove more cost efficient. As such, it is of interest to estimate the time until the occurrence of a potential churn for every customer in order to take preventive measures. The application of deep learning and machine learning to this type of problem using time series data is relatively new and there is a lot of recent research on this topic. This thesis is ba

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Svensson, Martin. "Unsupervised Segmentation of Time Series Data." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176519.

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In a modern vehicle system the amount of data generated are time series large enough for big data. Many of the time series contains interesting patterns, either densely populated or scarcely distributed over the data. For engineers to review the data a segmentation is crucial for data reduction, which is why this thesis investigates unsupervised segmentation of time series. This report uses two different methods, Fast Low-cost Unipotent Semantic Segmentation (FLUSS) and Information Gain-based Temporal Segmentation (IGTS). These have different approaches, shape and statistical respectively. Th

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Guthrey, Delparde Raleigh. "Time series analysis of ozone data." CSUSB ScholarWorks, 1998. https://scholarworks.lib.csusb.edu/etd-project/1788.

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MacDonald, Iain L. "Time series models for discrete data." Doctoral thesis, University of Cape Town, 1992. http://hdl.handle.net/11427/26105.

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Damle, Chaitanya. "Flood forecasting using time series data mining." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001038.

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Chintalapani, Gouthami. "Temporal treemaps for visualizing time series data." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1459.

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Thesis (M.S.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Xia, Betty Bin. "Similarity search in time series data sets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24275.pdf.

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Clarke, Liam. "Nonlinear time series analysis of data streams." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401147.

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Hills, Jonathan F. F. "Mining time-series data using discriminative subsequences." Thesis, University of East Anglia, 2014. https://ueaeprints.uea.ac.uk/53397/.

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Time-series data is abundant, and must be analysed to extract usable knowledge. Local-shape-based methods offer improved performance for many problems, and a comprehensible method of understanding both data and models. For time-series classification, we transform the data into a local-shape space using a shapelet transform. A shapelet is a time-series subsequence that is discriminative of the class of the original series. We use a heterogeneous ensemble classifier on the transformed data. The accuracy of our method is significantly better than the time-series classification benchmark (1-neares

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Borella, Margherita. "Time series analyses of consumption grouped data." Thesis, University College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271818.

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Hempel, Sabrina. "Deciphering gene regulation from time series data." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16602.

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Meine Arbeit beschäftigt sich mit der Rekonstruktion genregulatorischer Netze, um die Funktionalität von Organismen und ihre Reaktionen auf die vielfältigen externen Einflussfaktoren besser zu verstehen. Die Analyse kurzer, zeitaufgelöster Daten mit Hilfe von Assoziationsmaßen kann dabei erste wesentliche Einblicke in mögliche Wechselwirkungskreisläufe liefern. In einer umfangreicher Vergleichstudie untersuche ich die Effizienz der Netzwerkrekonstruktion bei der Anwendung verschiedener Maße und Bewertungsschemata. Weiterhin führe ich IOTA (inner composition alignment) als ein neues asymmetrisc

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Ferreira, Leonardo Nascimento. "Time series data mining using complex networks." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-01022018-144118/.

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A time series is a time-ordered dataset. Due to its ubiquity, time series analysis is interesting for many scientific fields. Time series data mining is a research area that is intended to extract information from these time-related data. To achieve it, different models are used to describe series and search for patterns. One approach for modeling temporal data is by using complex networks. In this case, temporal data are mapped to a topological space that allows data exploration using network techniques. In this thesis, we present solutions for time series data mining tasks using complex netw

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Sperl, Ryan E. "Hierarchical Anomaly Detection for Time Series Data." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590709752916657.

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Mitchell, F. "Painless knowledge acquisition for time series data." Thesis, University of Aberdeen, 1997. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU100889.

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Knowledge Acquisition has long been acknowledged as the bottleneck in producing Expert Systems. This is because, until relatively recently, the KA (Knowledge Acquisition) process has concentrated on extracting knowledge from a domain expert, which is a very time consuming process. Support tools have been constructed to help this process, but these have not been able to reduce the time radically. However, in many domains, the expert is not the only source of knowledge, nor indeed the best source of knowledge. This is particularly true in industrial settings where performance information is rout

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Tapinos, Avraam. "Time series data mining in systems biology." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/time-series-data-mining-in-systems-biology(5b538723-503b-4b82-959b-d4567e8d4658).html.

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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 represent

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Matsubara, Yasuko. "Statistical Data Mining for Time-series Datasets." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/157475.

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丁嘉慧 and Ka-wai Ting. "Time sequences: data mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226760.

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謝永然 and Wing-yin Tse. "Time series analysis in inventory management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977510.

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Pradhan, Shameer Kumar. "Investigation of Event-Prediction in Time-Series Data : How to organize and process time-series data for event prediction?" Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19416.

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The thesis determines the type of deep learning algorithms to compare for a particular dataset that contains time-series data. The research method includes study of multiple literatures and conduction of 12 tests. It deals with the organization and processing of the data so as to prepare the data for prediction of an event in the time-series. It also includes the explanation of the algorithms selected. Similarly, it provides a detailed description of the steps taken for classification and prediction of the event. It includes the conduction of multiple tests for varied timeframe in order to com

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Rekdal, Espen Ekornes. "Metric Indexing in Time Series." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10487.

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Jiang, Chunyu. "DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264.

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Morrill, Jeffrey P., and Jonathan Delatizky. "REAL-TIME RECOGNITION OF TIME-SERIES PATTERNS." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608854.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>This paper describes a real-time implementation of the pattern recognition technology originally developed by BBN [Delatizky et al] for post-processing of time-sampled telemetry data. This makes it possible to monitor a data stream for a characteristic shape, such as an arrhythmic heartbeat or a step-response whose overshoot is unacceptably large. Once programmed to recognize patterns of interest, it generates a symbolic description of a time-series signal in

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Milton, Robert. "Time-series in distributed real-time databases." Thesis, University of Skövde, Department of Computer Science, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-827.

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<p>In a distributed real-time environment where it is imperative to make correct decisions it is important to have all facts available to make the most accurate decision in a certain situation. An example of such an environment is an Unmanned Aerial Vehicle (UAV) system where several UAVs cooperate to carry out a certain task and the data recorded is analyzed after the completion of the mission. This project aims to define and implement a time series architecture for use together with a distributed real-time database for the ability to store temporal data. The result from this project is a tim

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Thornlow, Robert Timothy. "Spectrum estimation using extrapolated time series." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA246554.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 1990.<br>Thesis Advisor(s): Hippenstiel, Ralph. Second Reader: Tummala, Murali. "December 1990." Description based on title screen as viewed on March 30, 2010. DTIC Descriptor(s): Frequency, Density, Data Management, Models, Signal To Noise Ratio, Theses, Power Spectra, Sequences, Estimates, Short Range(Time), Spectra, Sampling, Fast Fourier Transforms, Extrapolation, Data Processing. DTIC Identifier(s): Power Spectra, Estimates, Time Series Analysis, Extrapolation, Density, Theses, Fast Fourier Transforms, Eigenvecto

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