Relevant bibliographies by topics / Time series data management

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  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 Decembe
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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
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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 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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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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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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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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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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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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