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Relevant bibliographies by topics / Time-varying data analysis

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Academic literature on the topic 'Time-varying data analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Time-varying data analysis"

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Colominas, Marcelo A., Mohamad El Sayed Hussein Jomaa, Nisrine Jrad, Anne Humeau-Heurtier, and Patrick Van Bogaert. "Time-Varying Time–Frequency Complexity Measures for Epileptic EEG Data Analysis." IEEE Transactions on Biomedical Engineering 65, no. 8 (2018): 1681–88. http://dx.doi.org/10.1109/tbme.2017.2761982.

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Murphy, Susan A., Gillian R. Bentley, and Mary Ann O'Hanesian. "An analysis for menstrual data with time-varying covariates." Statistics in Medicine 14, no. 17 (1995): 1843–57. http://dx.doi.org/10.1002/sim.4780141702.

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Jeong, Seonghyun, Minjae Park, and Taeyoung Park. "Analysis of binary longitudinal data with time-varying effects." Computational Statistics & Data Analysis 112 (August 2017): 145–53. http://dx.doi.org/10.1016/j.csda.2017.03.007.

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Kim, Yang-Jin. "Analysis of Recurrent Gap Time Data with a Binary Time-Varying Covariate." Communications for Statistical Applications and Methods 21, no. 5 (2014): 387–93. http://dx.doi.org/10.5351/csam.2014.21.5.387.

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JACOBS, CHRISTOPHER R., CLARE E. YELLOWLEY, DREW V. NELSON, and HENRY J. DONAHUE. "Analysis of Time-Varying Biological Data Using Rainflow Cycle Counting." Computer Methods in Biomechanics and Biomedical Engineering 3, no. 1 (2000): 31–40. http://dx.doi.org/10.1080/10255840008915252.

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Sutton, Eric, and Helen Na. "Time-varying reconstruction of the ionosphere. 2. Data source analysis." International Journal of Imaging Systems and Technology 9, no. 6 (1998): 491–99. http://dx.doi.org/10.1002/(sici)1098-1098(1998)9:6<491::aid-ima11>3.0.co;2-0.

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Li, Shanshan, Yifei Sun, Chiung-Yu Huang, Dean A. Follmann, and Richard Krause. "Recurrent event data analysis with intermittently observed time-varying covariates." Statistics in Medicine 35, no. 18 (2016): 3049–65. http://dx.doi.org/10.1002/sim.6901.

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Zivanovic, Miroslav. "Time-Varying Multicomponent Signal Modeling for Analysis of Surface EMG Data." IEEE Signal Processing Letters 21, no. 6 (2014): 692–96. http://dx.doi.org/10.1109/lsp.2014.2313880.

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Wong, May C. M., K. F. Lam, and Edward C. M. Lo. "Analysis of multilevel grouped survival data with time-varying regression coefficients." Statistics in Medicine 30, no. 3 (2010): 250–59. http://dx.doi.org/10.1002/sim.4094.

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Liu, Guochang, Sergey Fomel, and Xiaohong Chen. "Time-frequency analysis of seismic data using local attributes." GEOPHYSICS 76, no. 6 (2011): P23—P34. http://dx.doi.org/10.1190/geo2010-0185.1.

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Time-frequency analysis is an important technology in seismic data processing and interpretation. To localize frequency content in time, we have developed a novel method for computing a time-frequency map for nonstationary signals using an iterative inversion framework. We calculated time-varying Fourier coefficients by solving a least-squares problem that uses regularized nonstationary regression. We defined the time-frequency map as the norm of time-varying coefficients. Time-varying average frequency of the seismic data can also be estimated from the time-frequency map calculated by our met

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Dissertations / Theses on the topic "Time-varying data analysis"

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Ambler, Gareth. "Time varying-coefficient models." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321345.

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Mascarenhas, Ajith Arthur Snoeyink Jack. "Time-varying Reeb graphs a topological framework supporting the analysis of continuous time-varying data /." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,168.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.<br>Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science. Chapel Hill 2006." Discipline: Computer Science; Department/School: Computer Science.

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Chen, Chun-Ming. "Data Summarization for Large Time-varying Flow Visualization and Analysis." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469141137.

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Johansson, Jimmy. "Efficient Information Visualization of Multivariate and Time-Varying Data." Doctoral thesis, Linköping : Department of Science and Technology, Linköping University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11643.

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Woodring, Jonathan Lee. "Visualization of Time-varying Scientific Data through Comparative Fusion and Temporal Behavior Analysis." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243549189.

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Vargas, Aurea Rossy Soriano. "Visual exploration to support the identification of relevant attributes in time-varying multivariate data." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-23102018-115029/.

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Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of interest because its occurrence may affect the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to the phenomenon, generating a database of historical observations on the regional behavior of ionospheric scintillation. The analysis of such data is very challenging, since it consists of time-varying measurements of many variables which are heterogeneous

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Unsal, Ahmet Dundar. "Estimation Of Time-dependent Link Costs Using Gps Track Data." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12608010/index.pdf.

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Intelligent Transport Systems (ITS) are becoming a part of our daily lives in various forms of application. Their success depends highly on the accuracy of the digital data they use. In networks where characteristics change by time, time-based network analysis algorithms provide results that are more accurate. However, these analyses require time-based travel speed data to provide accurate results. Conventionally, traffic data are usually obtained using the data provided from loop-detectors. These detectors usually exist on main arteries, freeways and highways<br>they rarely exist on back road

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Dutta, Soumya. "In Situ Summarization and Visual Exploration of Large-scale Simulation Data Sets." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524070976058567.

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Frühwirth-Schnatter, Sylvia, Stefan Pittner, Andrea Weber, and Rudolf Winter-Ebmer. "Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering." Institute of Mathematical Statistics, 2018. http://dx.doi.org/10.1214/17-AOAS1132.

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In this paper we study data on discrete labor market transitions from Austria. In particular, we follow the careers of workers who experience a job displacement due to plant closure and observe - over a period of 40 quarters - whether these workers manage to return to a steady career path. To analyse these discrete-valued panel data, we apply a new method of Bayesian Markov chain clustering analysis based on inhomogeneous first order Markov transition processes with time-varying transition matrices. In addition, a mixtureof- experts approach allows us to model the probability of belong

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Parvinzamir, Farzad. "A visual analytics approach for visualisation and knowledge discovery from time-varying personal life data." Thesis, University of Bedfordshire, 2018. http://hdl.handle.net/10547/622697.

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Today, the importance of big data from lifestyles and work activities has been the focus of much research. At the same time, advances in modern sensor technologies have enabled self-logging of a signi cant number of daily activities and movements. Lifestyle logging produces a wide variety of personal data along the lifespan of individuals, including locations, movements, travel distance, step counts and the like, and can be useful in many areas such as healthcare, personal life management, memory recall, and socialisation. However, the amount of obtainable personal life logging data has enormo

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Books on the topic "Time-varying data analysis"

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Tobia, John. A time-varying analysis of the exponentially data weighted recursive least squares (EDW-RLS) algorithm. National Library of Canada, 1992.

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Watson, Peter. Survival analysis. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0018.

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This chapter explores survival analysis. It includes data censoring, functions of duration time (the survival function, and hazard function), Cox’s proportional hazards model, log-linearity, time varying predictors, and odds ratios.

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Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.

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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as li

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Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.

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This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance

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Buu, Anne, and Runze Li. New Statistical Methods Inspired by Data Collected from Alcohol and Substance Abuse Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676001.003.0021.

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This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity an

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Prado, Raquel. Multistate models for mental fatigue. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.29.

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This article discusses the use of structured, multivariate Bayesian dynamic models in the analysis of experimental data involving large-scale electroencephalography (EEG) signals or time series generated on individuals subject to tasks inducing mental fatigue. It first provides an overview of the goals and challenges in the analysis of brain signals, using the EEG case as example, before describing the development and application of novel time-varying autoregressive and regime switching models, which incorporate relevant prior information via structured priors and fitted using novel, customize

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Kraberg, Alexandra, Katja Metfies, and Rowena Stern. Sampling, Preservation and Counting of Samples I: Phytoplankton. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199233267.003.0009.

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This chapter reviews phytoplankton sampling and analysis techniques, discussing them in light of their advantages and disadvantages. Different sampling methods have varying levels of precision and accuracy. This means that they affect the ways in which individual data sets can be interpreted, and methods therefore have to be kept consistent within time series to avoid creating artefacts. The discussions cover qualitative and semi-quantitative methods, quantitative methods, sample analysis, automated/semi-automated systems, and molecular methodologies. None of the methods are universally applic

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Book chapters on the topic "Time-varying data analysis"

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Lewandowski, Achim, and Peter Protzel. "Predicting Time-Varying Functions with Local Models." In Advances in Intelligent Data Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44816-0_5.

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Chiogna, Monica, and Carlo Gaetan. "Some Applications of Time-Varying Coefficient Models to Count Data." In Between Data Science and Applied Data Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-18991-3_21.

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Boyd, Sarah. "Detecting and describing patterns in time-varying data using wavelets." In Advances in Intelligent Data Analysis Reasoning about Data. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052873.

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Matsumoto, Kazunori, and Kazuo Hashimoto. "Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic." In Advances in Intelligent Data Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48412-4_11.

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Hachaj, Tomasz, Marek R. Ogiela, Marcin Piekarczyk, and Katarzyna Koptyra. "Averaging Three-Dimensional Time-Varying Sequences of Rotations: Application to Preprocessing of Motion Capture Data." In Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_2.

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Camarero, Mariam, Juan Sapena, and Cecilio Tamarit. "An Analysis of the Time-Varying Behavior of the Equilibrium Velocity of Money in the Euro Area." In Recent Econometric Techniques for Macroeconomic and Financial Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54252-8_5.

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Li, Caicai, Hao Jin, and Huihui Bai. "Ratio Test for Time-Varying Variance of Mean Change Point Based on Big Data Analysis." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70042-3_53.

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Krasotkina, Olga, Vadim Mottl, Michael Markov, Elena Chernousova, and Dmitry Malakhov. "Methods of Hyperparameter Estimation in Time-Varying Regression Models with Application to Dynamic Style Analysis of Investment Portfolios." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62416-7_31.

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Borms, Samuel, Kris Boudt, Frederiek Van Holle, and Joeri Willems. "Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies." In Data Science for Economics and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_10.

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AbstractWe present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.

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Tan, Manchun, Zhiqiang Song, and Xuemei Zhang. "Stability of Complex-Valued Neutral-Type Neural Networks with Time-Varying Delays." In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23756-1_49.

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Conference papers on the topic "Time-varying data analysis"

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Goglia, Lorenzo, and Eugenio Zimeo. "Contact-Tracing based on Time-Varying Graphs Analysis." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377835.

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Tong, Xin, Teng-Yok Lee, and Han-Wei Shen. "Salient time steps selection from large scale time-varying data sets with dynamic time warping." In 2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2012. http://dx.doi.org/10.1109/ldav.2012.6378975.

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Martin, Steven, and Han-Wei Shen. "Histogram spectra for multivariate time-varying volume LOD selection." In 2011 IEEE Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2011. http://dx.doi.org/10.1109/ldav.2011.6092315.

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Bridges, Robert A., John P. Collins, Erik M. Ferragut, Jason A. Laska, and Blair D. Sullivan. "Multi-Level Anomaly Detection on Time-Varying Graph Data." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. ACM, 2015. http://dx.doi.org/10.1145/2808797.2809406.

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Wang, Zi, Yun Guo, and Haijun Gong. "An Integrative Analysis of Time-varying Regulatory Networks From High-dimensional Data." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622361.

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Hochin, Teruhisa. "Dissimilarity and retrieval of time-varying data towards big data analysis." In 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2015. http://dx.doi.org/10.1109/snpd.2015.7176167.

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Min Shih, Yubo Zhang, Kwan-Liu Ma, Jayanarayanan Sitaraman, and Dimitri Mavriplis. "Out-of-core visualization of time-varying hybrid-grid volume data." In 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2014. http://dx.doi.org/10.1109/ldav.2014.7013209.

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Tory, Melanie K., Torsten Moeller, and M. Stella Atkins. "Visualization of time-varying MRI data for MS lesion analysis." In Medical Imaging 2001, edited by Seong K. Mun. SPIE, 2001. http://dx.doi.org/10.1117/12.428104.

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Widanagamaachchi, W., C. Christensen, P. T. Bremer, and Valerio Pascucci. "Interactive exploration of large-scale time-varying data using dynamic tracking graphs." In 2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV 2012). IEEE, 2012. http://dx.doi.org/10.1109/ldav.2012.6378962.

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Dasgupta, Aritra, Robert Kosara, and Luke Gosink. "Meta parallel coordinates for visualizing features in large, high-dimensional, time-varying data." In 2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2012. http://dx.doi.org/10.1109/ldav.2012.6378980.

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Reports on the topic "Time-varying data analysis"

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Mascarenhas, Ajith Arthur. Time-varying Reeb Graphs: A Topological Framework Supporting the Analysis of Continuous Time-varying Data. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/900445.

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Wright, Kirsten. Collecting Plant Phenology Data In Imperiled Oregon White Oak Ecosystems: Analysis and Recommendations for Metro. Portland State University, 2020. http://dx.doi.org/10.15760/mem.64.

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Highly imperiled Oregon white oak ecosystems are a regional conservation priority of numerous organizations, including Oregon Metro, a regional government serving over one million people in the Portland area. Previously dominant systems in the Pacific Northwest, upland prairie and oak woodlands are now experiencing significant threat, with only 2% remaining in the Willamette Valley in small fragments (Hulse et al. 2002). These fragments are of high conservation value because of the rich biodiversity they support, including rare and endemic species, such as Delphinium leucophaeum (Oregon Depart

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de Vries, Gaaitzen, Linda Arfelt, Dorothea Drees, et al. The Economic Transformation Database (ETD): content, sources, and methods. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/wtn/2021-2.

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This note introduces the GGDC/UNU-WIDER Economic Transformation Database (ETD), which provides time series of employment and real and nominal value added by 12 sectors in 51 countries for the period 1990–2018. The ETD includes 20 Asian, 9 Latin American, 4 Middle-East and North African, and 18 sub-Saharan African countries at varying levels of economic development. The ETD is constructed on the basis of an in-depth investigation of the availability and usability of statistical sources on a country-by-country basis. The ETD provides researchers with data to analyse the variety and determinants

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Komppula, Birgitta, Tomi Karppinen, Henrik Virta, et al. Air quality in Finland according to air quality measurements and satellite observations. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361409.

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In this report the current air quality in Finland has been assessed with air quality measurement data and satellite observations. The assessment of ambient air concentrations included following air impurities: NO2, NOx, PM10, PM2,5, SO2, CO, O3, benzo(a)pyrene, benzene, Pb, As, Cd ja Ni. For these pollutants air quality assessment thresholds are given in air quality legislation (2008/50/EY, 2004/107/EY). Assessment has been performed for air quality zones. The main data set included air quality measurements performed in Finland during 2015–2019. Satellite observations were used as an objective

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