Relevant bibliographies by topics / Time series data management

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Time series data management'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Time series data management"

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Biem, A., H. Feng, A. V. Riabov, and D. S. Turaga. "Real-time analysis and management of big time-series data." IBM Journal of Research and Development 57, no. 3/4 (2013): 8:1–8:12. http://dx.doi.org/10.1147/jrd.2013.2243551.

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Mahaney, John K., N. A. Jr., David Lee Baker, James H. Hamburg, and David E. Booth. "Time series analysis of process data." International Journal of Operational Research 2, no. 3 (2007): 231. http://dx.doi.org/10.1504/ijor.2007.012851.

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Rasmussen, Rasmus. "On time series data and optimal parameters." Omega 32, no. 2 (2004): 111–20. http://dx.doi.org/10.1016/j.omega.2003.09.013.

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Zhou, Qifeng, Ruyuan Han, Tao Li, and Bin Xia. "Joint prediction of time series data in inventory management." Knowledge and Information Systems 61, no. 2 (2019): 905–29. http://dx.doi.org/10.1007/s10115-018-1302-y.

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Cuffe, Paul. "Playing Fair With Time Series Data." IEEE Potentials 39, no. 6 (2020): 47–50. http://dx.doi.org/10.1109/mpot.2018.2868000.

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Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.
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Inniss, Tasha R. "Seasonal clustering technique for time series data." European Journal of Operational Research 175, no. 1 (2006): 376–84. http://dx.doi.org/10.1016/j.ejor.2005.03.049.

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Zhang, Kaimeng, Chi Tim Ng, and Myung Hwan Na. "Real time prediction of irregular periodic time series data." Journal of Forecasting 39, no. 3 (2020): 501–11. http://dx.doi.org/10.1002/for.2637.

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Patterson, K. D. "Exploiting information in vintages of time-series data." International Journal of Forecasting 19, no. 2 (2003): 177–97. http://dx.doi.org/10.1016/s0169-2070(01)00145-5.

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Welch, Eric, Stuart Bretschneider, and John Rohrbaugh. "Accuracy of judgmental extrapolation of time series data." International Journal of Forecasting 14, no. 1 (1998): 95–110. http://dx.doi.org/10.1016/s0169-2070(97)00055-1.

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

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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 being used in this type of investigation and becoming a valuable resource in today�s organisations. This thesis investigates and proposes solutions to some current and important issues in time series data management (TSDM), using Design Science Research Methodology. The thesis presents new models for mapping time series data to relational databases which optimise the use of disk space, can handle different time granularities, status attributes, and facilitate time series data manipulation in a commercial Relational Database Management System (RDBMS). These new models provide a good solution for current time series database applications with RDBMS and are tested with a case study and prototype with financial time series information. Also included is a temporal data model for illustrating time series data lifetime behaviour based on a new set of time dimensions (confidentiality, definitiveness, validity, and maturity times) specially targeted to manage time series data which are introduced to correctly represent the different status of time series data in a timeline. The proposed temporal data model gives a clear and accurate picture of the time series data lifecycle. Formal definitions of these time series dimensions are also presented. In addition, a time series grouping mechanism in an extensible commercial relational database system is defined, illustrated, and justified. The extension consists of a new data type and its corresponding rich set of routines that support modelling and operating time series information within a higher level of abstraction. It extends the capability of the database server to organise and manipulate time series into groups. Thus, this thesis presents a new data type that is referred to as GroupTimeSeries, and its corresponding architecture and support functions and operations. Implementation options for the GroupTimeSeries data type in relational based technologies are also presented. Finally, a framework for TSDM with enough expressiveness of the main requirements of time series application and the management of that data is defined. The framework aims at providing initial domain know-how and requirements of time series data management, avoiding the impracticability of designing a TSDM system on paper from scratch. Many aspects of time series applications including the way time series data are organised at the conceptual level are addressed. The central abstraction for the proposed domain specific framework is the notions of business sections, group of time series, and time series itself. The framework integrates comprehensive specification regarding structural and functional aspects for time series data management. A formal framework specification using conceptual graphs is also explored.
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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 three key components. The first is a metadatabase to hold metadata for the raster datasets, and an interface to manage the metadatabase and facilitate the search and discovery of raster metadata. The design of the metadatabase involved the examination of existing standards for geographic raster metadata and the determination of the metadata elements required for time-series raster data. The second component is an interactive tool for viewing the time-series raster data discovered via the metadatabase. The third component provides basic image analysis functionality typically required by users of time-series raster datasets. A prototype was implemented using open source software and following the Open Geospatial Consortium specifications for web map services (WMS) version 1.3.0. After implementation, an evaluation of the prototype was carried out by the target users from the RRSU (Regional Remote Sensing Unit) to assess the usability, the added value of the prototype and its impact on the work of the users. The evaluation showed that the prototype system was generally well received, since it allowed both the data managers and users of time-series datasets to save significant amounts of time in their work routines and it also offered some raster data analyses that are useful to a wider community of time-series raster data managers.
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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 this today we stive to examine the field and study the prior work in time series management. We then propose a large system capable of handling time series management end to end, from generation to consumption by the end user. Our system is composed of open-source data processing frameworks. Our system has the capability to collect time series data, perform stream processing over it, store it for immediate and future processing and create necessary visualizations. We present the implementation of the system and perform experimentations to show its scalability to handle growing pipelines of incoming data from various sources.
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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 this exact trail, by processing external IoT-acquired data, going through a cleansing and a processing phase in order to obtain neural networks ready-to-feed data. The sewerage project acquires signals from IoT sensors of a sewerage infrastructure and aims at predicting signals' trends over close future periods. The aqueduct project acquires the same information type from aqueduct plants and aims to reduce the false alarm rate of the telecontrol system. Given the good results of both projects it can be concluded that the data processing phase has produced high-quality information which is the main objective of this thesis.
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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 the cooperation of a major international financial firm. The use of their actual historical financial credit risk data sets significantly aided this work by providing genuine feedback, validating specific results, and confirming the usefulness of the method. While illustrated through the use of credit risk data sets, the methodology this thesis presents is designed to be applied easily and transparently to structured data sets used for time series analysis.<br>by Carlos Alvidrez.<br>S.M. in Engineering and Management
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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 attraverso i criteri sopracitati. Una comparazione, anche tramite forma tabellare, è stata accompagnata da una descrizione che potesse guidare il lettore ad una comprensione rapida delle differenze, dei punti di forza e delle debolezze di ogni TSDB. Infine sono state tratte le conclusioni che, in seguito al percorso svolto, sono sembrate più appropriate, sono stati individuati dei punti chiave su cui incentrare i lavori futuri e sono stati proposti altri spunti di lavoro ai quali non si è potuto lavorare per mancanza di ulteriore tempo e di disponibilità dei software completi di tutte le loro funzionalità.
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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 techniques et des algorithmes d’indexation correspondants, afin de relever les défis de l’évolutivité lors de la recherche de similarité.Les analystes, afin de s’acquitter efficacement de leurs tâches, ont besoin de systèmes d’analyse visuelle interactifs, extrêmement rapides, et puissants. Lors de la création de tels systèmes, nous avons identifié deux principaux défis: (1) la perception de similarité et (2) la recherche progressive de similarité. Le premier traite de la façon dont les gens perçoivent des modèles similaires et du rôle de la visualisation dans la perception de similarité. Le dernier point concerne la rapidité avec laquelle nous pouvons redonner aux utilisateurs des mises à jour des résultats progressifs, lorsque les temps de réponse du système sont longs et non interactifs. Le but de cette thèse est de répondre et de donner des solutions aux défis ci-dessus.Dans la première partie, nous avons étudié si différentes représentations visuelles (Graphiques en courbes, Graphiques d’horizon et Champs de couleur) modifiaient la perception de similarité des séries temporelles. Nous avons essayé de comprendre si les résultats de recherche automatique de similarité sont perçus de manière similaire, quelle que soit la technique de visualisation; et si ce que les gens perçoivent comme similaire avec chaque visualisation s’aligne avec différentes mesures de similarité. Nos résultats indiquent que les Graphes d’horizon s’alignent sur des mesures qui permettent des variations de décalage temporel ou d’échelle (i.e., ils promeuvent la déformation temporelle dynamique). En revanche, ils ne s’alignent pas sur des mesures autorisant des variations d’amplitude et de décalage vertical (ils ne promeuvent pas des mesures basées sur la z-normalisation). L’inverse semble être le cas pour les Graphiques en courbes et les Champs de couleur. Dans l’ensemble, nos travaux indiquent que le choix de la visualisation affecte les schémas temporels que l’homme considère comme similaires. Donc, la notion de similarité dans les séries temporelles est dépendante de la technique de visualisation.Dans la deuxième partie, nous nous sommes concentrés sur la recherche progressive de similarité dans de grandes séries de données. Nous avons étudié la rapidité avec laquelle les premières réponses approximatives et puis des mises à jour des résultats progressifs sont détectées lors de l’exécuton des requêtes progressives. Nos résultats indiquent qu’il existe un écart entre le moment où la réponse finale s’est trouvée et le moment où l’algorithme de recherche se termine, ce qui entraîne des temps d’attente gonflés sans amélioration. Des estimations probabilistes pourraient aider les utilisateurs à décider quand arrêter le processus de recherche, i.e., décider quand l’amélioration de la réponse finale est improbable. Nous avons développé et évalué expérimentalement une nouvelle méthode probabiliste qui calcule les garanties de qualité des résultats progressifs de k-plus proches voisins (k-NN). Notre approche apprend d’un ensemble de requêtes et construit des modèles de prédiction basés sur deux observations: (i) des requêtes similaires ont des réponses similaires; et (ii) des réponses progressives renvoyées par les indices de séries de données sont de bons prédicteurs de la réponse finale. Nous fournissons des estimations initiales et progressives de la réponse finale<br>Time series are becoming ubiquitous in modern life, and given their sizes, their analysis is becoming increasingly challenging. Time series analysis involves tasks such as pattern matching, anomaly detection, frequent pattern identification, and time series clustering or classification. These tasks rely on the notion of time series similarity. The data-mining community has proposed several techniques, including many similarity measures (or distance measure algorithms), for calculating the distance between two time series, as well as corresponding indexing techniques and algorithms, in order to address the scalability challenges during similarity search.To effectively support their tasks, analysts need interactive visual analytics systems that combine extremely fast computation, expressive querying interfaces, and powerful visualization tools. We identified two main challenges when considering the creation of such systems: (1) similarity perception and (2) progressive similarity search. The former deals with how people perceive similar patterns and what the role of visualization is in time series similarity perception. The latter is about how fast we can give back to users updates of progressive similarity search results and how good they are, when system response times are long and do not support real-time analytics in large data series collections. The goal of this thesis, that lies at the intersection of Databases and Human-Computer Interaction, is to answer and give solutions to the above challenges.In the first part of the thesis, we studied whether different visual representations (Line Charts, Horizon Graphs, and Color Fields) alter time series similarity perception. We tried to understand if automatic similarity search results are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic similarity measures and their similarity constraints. Our findings indicate that Horizon Graphs promote as invariant local variations in temporal position or speed, and as a result they align with measures that allow variations in temporal shifting or scaling (i.e., dynamic time warping). On the other hand, Horizon Graphs do not align with measures that allow amplitude and y-offset variations (i.e., measures based on z-normalization), because they exaggerate these differences, while the inverse seems to be the case for Line Charts and Color Fields. Overall, our work indicates that the choice of visualization affects what temporal patterns humans consider as similar, i.e., the notion of similarity in time series is visualization-dependent.In the second part of the thesis, we focused on progressive similarity search in large data series collections. We investigated how fast first approximate and then updates of progressive answers are detected, while we execute similarity search queries. Our findings indicate that there is a gap between the time the final answer is found and the time when the search algorithm terminates, resulting in inflated waiting times without any improvement. Computing probabilistic estimates of the final answer could help users decide when to stop the search process. We developed and experimentally evaluated using benchmarks, a new probabilistic learning-based method that computes quality guarantees (error bounds) for progressive k-Nearest Neighbour (k-NN) similarity search results. Our approach learns from a set of queries and builds prediction models based on two observations: (i) similar queries have similar answers; and (ii) progressive best-so-far (bsf) answers returned by the state-of-the-art data series indexes are good predictors of the final k-NN answer. We provide both initial and incrementally improved estimates of the final answer
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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 less importance than do the internal factors. Scholars of the developing world have argued that political, social and economic conditions in the society are as important as internal factors. These factors may have a very significant influence on the enterprises and on the society as a whole. Consequently, public enterprises in developing countries always encounter the same problem of operating inefficiency. The RSR is selected as a case study because of its advantages over the other public enterprises in Thailand in terms of size of operation, length of service, and data availability. For the purpose of this project, data are collected from 1960 to 1984 for longitudinal analysis. The methods of analysis are divided into two major sections: simple regression testing and multiple regression testing. The principal component technique is used in both testings to reduce variables to a smaller number for further analysis. The simple regression tests yielded mixed results, but the multiple regression tests resulted in significant relationships. The three new factors derived from the factor analysis technique were labeled as "the organizational pressures," "the socio-political downturn," and "the public criticisms." They explained 84% of all the variance of operating efficiency. The other 16% was the effect of other factors including the management skills, which were excluded from this analysis.
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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 forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.
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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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Books on the topic "Time series data management"

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Statistics Canada. Current Economic Analysis Division. CANSIM (Canadian socio-economic information management system): Mini base series directory. Statistics Canada, 1987.

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Agung, I. Gusti Ngurah. Advanced Time Series Data Analysis. John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119504818.

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Time series modeling of neuroscience data. Taylor & Francis, 2012.

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Time series data analysis using EViews. Wiley, 2009.

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Spectral analysis of time-series data. Guilford Press, 1998.

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Ozaki, Tohru. Time series modeling of neuroscience data. Taylor & Francis, 2012.

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Statistical Office of the European Communities. CRONOS: Data bank for macroeconomic time series. Office for Official Publications of the European Communities], 1985.

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Tsiantas, Ioannis. Time series analysis of stock market data. UMIST, 1995.

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Time series analysis. Duxbury Press, 1986.

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Privalʹskiĭ, V. E. Time series analysis package: Autoregressive time and frequency domains analysis of scalar and multi-variate time series. Utah Climate Center, Utah State University, 1993.

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Book chapters on the topic "Time series data management"

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Pole, Andy, Mike West, and Jeff Harrison. "Tutorial: Data Management." In Applied Bayesian Forecasting and Time Series Analysis. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3432-1_13.

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Stojkov, Marinko, Vladimir Mikuličić, and Srete Nikolovski. "Power System Fault Data and Time Series." In Probabilistic Safety Assessment and Management. Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_208.

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Dannecker, Lars. "The Current State of Energy Data Management and Forecasting." In Energy Time Series Forecasting. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-11039-0_3.

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Mallqui, Dennys, and Ricardo A. S. Fernandes. "Recurrence Plot Representation for Multivariate Time-Series Analysis." In Information Management and Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46140-9_3.

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Gao, He, Xiao-li Cai, and Yu Fei. "Time Series Data Modeling and Application." In The 19th International Conference on Industrial Engineering and Engineering Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38427-1_115.

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Pojarliev, M., and W. Polasek. "Portfolio Management Using Multivariate Time Series Forecasts." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55721-7_52.

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Liu, Yubao, Xiuwei Chen, Fei Wang, and Jian Yin. "Efficient Detection of Discords for Time Series Stream." In Advances in Data and Web Management. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00672-2_62.

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Lv, Jianjiang, Jianbo Yuan, Minh Vo, and Junliang Zhang. "Hydrate Management with Real-Time Data Visualization." In Springer Series in Geomechanics and Geoengineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7560-5_8.

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Tang, Bo, Man Lung Yiu, Yuhong Li, and Leong Hou U. "Exploit Every Cycle: Vectorized Time Series Algorithms on Modern Commodity CPUs." In Data Management on New Hardware. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56111-0_2.

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Armstrong, J. Scott. "Extrapolation for Time-Series and Cross-Sectional Data." In International Series in Operations Research & Management Science. Springer US, 2001. http://dx.doi.org/10.1007/978-0-306-47630-3_11.

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Conference papers on the topic "Time series data management"

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Matus-Castillejos, A., and R. Jentzsch. "A time series data management framework." In International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II. IEEE, 2005. http://dx.doi.org/10.1109/itcc.2005.45.

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Li, Yuhong. "Efficient Query Processing in Time Series." In SIGMOD/PODS'15: International Conference on Management of Data. ACM, 2015. http://dx.doi.org/10.1145/2744680.2744688.

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Petrov, Daniel, Rakan Alseghayer, Mohamed Sharaf, Panos K. Chrysanthis, and Alexandros Labrinidis. "Interactive Exploration of Correlated Time Series." In SIGMOD/PODS'17: International Conference on Management of Data. ACM, 2017. http://dx.doi.org/10.1145/3077331.3077335.

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Tiano, Donato, Angela Bonifati, and Raymond Ng. "FeatTS: Feature-based Time Series Clustering." In SIGMOD/PODS '21: International Conference on Management of Data. ACM, 2021. http://dx.doi.org/10.1145/3448016.3452757.

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Abildgaard, Nicolaj Casanova, Casper Weiss Bang, Jonas Hansen, et al. "A Correlated Time Series Forecast System." In 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 2020. http://dx.doi.org/10.1109/mdm48529.2020.00054.

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Yang, Peilin, Srikanth Thiagarajan, and Jimmy Lin. "Robust, Scalable, Real-Time Event Time Series Aggregation at Twitter." In SIGMOD/PODS '18: International Conference on Management of Data. ACM, 2018. http://dx.doi.org/10.1145/3183713.3190663.

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Peng, Jinglin, Hongzhi Wang, Jianzhong Li, and Hong Gao. "Set-based Similarity Search for Time Series." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2882963.

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Neamtu, Rodica, Ramoza Ahsan, Charles Lovering, Cuong Nguyen, Elke Rundensteiner, and Gabor Sarkozy. "Interactive Time Series Analytics Powered by ONEX." In SIGMOD/PODS'17: International Conference on Management of Data. ACM, 2017. http://dx.doi.org/10.1145/3035918.3058729.

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Chen, Yiru, and Silu Huang. "TSExplain: Surfacing Evolving Explanations for Time Series." In SIGMOD/PODS '21: International Conference on Management of Data. ACM, 2021. http://dx.doi.org/10.1145/3448016.3452769.

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Sakurai, Yasushi, Yasuko Matsubara, and Christos Faloutsos. "Mining and Forecasting of Big Time-series Data." In SIGMOD/PODS'15: International Conference on Management of Data. ACM, 2015. http://dx.doi.org/10.1145/2723372.2731081.

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Reports on the topic "Time series data management"

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Latifovic, R., D. Pouliot, L. Sun, J. Schwarz, and W. Parkinson. Moderate resolution time series data management and analysis: automated large area mosaicking and quality control. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2015. http://dx.doi.org/10.4095/296204.

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Stracuzzi, David, Matthew Peterson, and Gabriel Popoola. Measuring and Extracting Activity from Time Series Data. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1673451.

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McDonough, J. M., S. Mukerji, and S. Chung. A data-fitting procedure for chaotic time series. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/677199.

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Graham, Marc H. Issues in Real-Time Data Management. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240712.

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Frye, Daniel E., W. R. Geyer, and Bradford Butman. Low Cost Modular Telemetry for Coastal Time-Series Data. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada399201.

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Beam, Craig A., Emily F. Conant, Harold L. Kundel, Ji-Hyun Lee, Patricia A. Romily, and Edward A. Sickles. Time-Series Analysis of Human Interpretation Data in Mammography. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada434583.

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Venugopal, Niveditha. Annotation-Enabled Interpretation and Analysis of Time-Series Data. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6592.

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Eichenbaum, Martin, and Lars Peter Hansen. Estimating Models with Intertemporal Substitution Using Aggregate Time Series Data. National Bureau of Economic Research, 1987. http://dx.doi.org/10.3386/w2181.

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HYDROLOGIC ENGINEERING CENTER DAVIS CA. Statistical Analysis of Time Series Data (STATS). Users Manual (Preliminary). Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada204568.

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Tian, X., Y. Fan, and C. Kamath. Towards Detecting Motifs in Time Series Data of Wind Energy. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1059072.

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