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Littérature scientifique sur le sujet « Time-series analysis »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 9 juin 2025

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Articles de revues sur le sujet "Time-series analysis"

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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 (January 28, 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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Lutsenko, V. V., N. N. Kucherov, and A. V. Gladkov. "Predicting traffic congestion based on time series analysis." Sovremennaya nauka i innovatsii, no. 2 (42) (2023): 50–58. http://dx.doi.org/10.37493/2307-910x.2023.2.5.

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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion.

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Lutsenko, V. V., N. N. Kucherov, and A. V. Gladkov. "PREDICTING TRAFFIC CONGESTION BASED ON TIME SERIES ANALYSIS." Sovremennaya nauka i innovatsii, no. 1 (41) (2023): 47–55. http://dx.doi.org/10.37493/2307-910x.2023.1.4.

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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion

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Bowerman, Bruce, and Jonathan D. Cryer. "Time Series Analysis." Technometrics 29, no. 2 (May 1987): 240. http://dx.doi.org/10.2307/1269781.

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Donatelli, Richard E., Ji-Ae Park, Spencer M. Mathews, and Shin-Jae Lee. "Time series analysis." American Journal of Orthodontics and Dentofacial Orthopedics 161, no. 4 (April 2022): 605–8. http://dx.doi.org/10.1016/j.ajodo.2021.07.013.

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Potscher, Benedikt M., and James D. Hamilton. "Time Series Analysis." Journal of the American Statistical Association 91, no. 433 (March 1996): 439. http://dx.doi.org/10.2307/2291435.

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Bakouch, Hassan S. "Time Series Analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 1 (January 2009): 283. http://dx.doi.org/10.1111/j.1467-985x.2008.00571_4.x.

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Subba Rao, T. "Time Series Analysis." Journal of Time Series Analysis 31, no. 2 (March 2010): 139. http://dx.doi.org/10.1111/j.1467-9892.2009.00641.x.

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Breitung, Jorg, and James D. Hamilton. "Time Series Analysis." Contemporary Sociology 24, no. 2 (March 1995): 271. http://dx.doi.org/10.2307/2076916.

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Taylor, Diana. "Time-Series Analysis." Western Journal of Nursing Research 12, no. 2 (April 1990): 254–61. http://dx.doi.org/10.1177/019394599001200210.

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Thèses sur le sujet "Time-series analysis"

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Pope, Kenneth James. "Time series analysis." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318445.

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Yin, Jiang Ling. "Financial time series analysis." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.

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Gore, Christopher Mark. "A time series classifier." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/Gore_09007dcc804e6461.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2008.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 29, 2008) Includes bibliographical references (p. 53-55).

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Lam, Vai Iam. "Time domain approach in time series analysis." Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446633.

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Malan, Karien. "Stationary multivariate time series analysis." Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-06132008-173800.

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Huang, Naijing. "Essays in time series analysis." Thesis, Boston College, 2015. http://hdl.handle.net/2345/bc-ir:104627.

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Thesis advisor: Zhijie Xiao<br>I have three chapters in my dissertation. The first chapter is about the estimation and inference for DSGE model; the second chapter is about testing financial contagion among stock markets, and in the last chapter, I propose a new econometrics method to forecast inflation interval. This first chapter studies proper inference and asymptotically accurate structural break tests for parameters in Dynamic Stochastic General Equilibrium (DSGE) models in a maximum likelihood framework. Two empirically relevant issues may invalidate the conventional inference procedures and structural break tests for parameters in DSGE models: (i) weak identification and (ii) moderate parameter instability. DSGE literatures focus on dealing with weak identification issue, but ignore the impact of moderate parameter instability. This paper contributes to the literature via considering the joint impact of two issues in DSGE framework. The main results are: in a weakly identified DSGE model, (i) moderate instability from weakly identified parameters would not affect the validity of standard inference procedures or structural break tests; (ii) however, if strongly identified parameters are featured with moderate time-variation, the asymptotic distributions of test statistics would deviate from standard ones and would no longer be nuisance parameter free, which renders standard inference procedures and structural break tests invalid and provides practitioners misleading inference results; (iii) as long as I concentrate out strongly identified parameters, the instability impact of them would disappear as the sample size goes to infinity, which recovers the power of conventional inference procedure and structural break tests for weakly identified parameters. To illustrate my results, I simulate and estimate a modified version of the Hansen (1985) Real Business Cycle model and find that my theoretical results provide reasonable guidance for finite sample inference of the parameters in the model. I show that confidence intervals that incorporate weak identification and moderate parameter instability reduce the biases of confidence intervals that ignore those effects. While I focus on DSGE models in this paper, all of my theoretical results could be applied to any linear dynamic models or nonlinear GMM models. The second chapter, regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test allows us to investigate the stock market contagion at various quantiles, not only at the mean. We show that the quantile contagion test can detect a contagion effect that is possibly ignored by correlation-based tests. A wide range of simulation studies show that the proposed test is superior to the correlation-based tests in terms of size and power. We compare our test with correlation-based tests using three real data sets: the 1994 Tequila crisis, the 1997 Asia crisis, and the 2001 Argentina crisis. Empirical results show substantial differences between two types of tests. In the third chapter, I use Quantile Bayesian Approach-- to do the interval forecast for inflation in the semi-parametric framework. This new method introduces Bayesian solution to the quantile framework for two reasons: 1. It enables us to get more efficient quantile estimates when the informative prior is used (He and Yang (2012)); 2. We use Markov Chain Monte Carlo (MCMC) algorithm to generate samples of the posterior distribution for unknown parameters and take the mean or mode as the estimates. This MCMC estimator takes advantage of numerical integration over the standard numerical differentiation based optimization, especially when the likelihood function is complicated and multi-modal. Simulation results find better interval forecasting performance of Quantile Bayesian Approach than commonly used parametric approach<br>Thesis (PhD) — Boston College, 2015<br>Submitted to: Boston College. Graduate School of Arts and Sciences<br>Discipline: Economics

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Alagon, J. "Discriminant analysis for time series." Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375222.

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Warnes, Alexis. "Diagnostics in time series analysis." Thesis, Durham University, 1994. http://etheses.dur.ac.uk/5159/.

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The portmanteau diagnostic test for goodness of model fit is studied. It is found that the true variances of the estimated residual autocorrelation function are potentially deflated considerably below their asymptotic level, and exhibit high correlations with each other. This suggests a new portmanteau test, ignoring the first p + q residual autocorrelation terms and hence approximating the asymptotic chi-squared distribution more closely. Simulations show that this alternative portmanteau test produces greater accuracy in its estimated significance levels, especially in small samples. Theory and discussions follow, pertaining to both the Dynamic Linear Model and the Bayesian method of forecasting. The concept of long-term equivalence is defined. The difficulties with the discounting approach in the DLM are then illustrated through an example, before deriving equations for the step-ahead forecast distribution which could, instead, be used to estimate the evolution variance matrix W(_t). Non-uniqueness of W in the constant time series DLM is the principal drawback with this idea; however, it is proven that in any class of long-term equivalent models only p degrees of freedom can be fixed in W, leading to a potentially diagonal form for this matrix. The bias in the k(^th) step-ahead forecast error produced by any TSDLM variance (mis)specification is calculated. This yields the variances and covariances of the forecast error distribution; given sample estimates of these, it proves possible to solve equations arising from these calculations both for V and p elements of W. Simulations, and a "head-to-head" comparison, for the frequently-applied steady model illustrate the accuracy of the predictive calculations, both in the convergence properties of the sample (co)variances, and the estimates Ṽ and Ŵ. The method is then applied to a 2-dimensional constant TSDLM. Further simulations illustrate the success of the approach in producing accurate on-line estimates for the true variance specifications within this widely-used model.

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Chan, Hon Tsang. "Discriminant analysis of time series." Thesis, University of Newcastle Upon Tyne, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315614.

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Fulcher, Benjamin D. "Highly comparative time-series analysis." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:642b65cf-4686-4709-9f9d-135e73cfe12e.

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In this thesis, a highly comparative framework for time-series analysis is developed. The approach draws on large, interdisciplinary collections of over 9000 time-series analysis methods, or operations, and over 30 000 time series, which we have assembled. Statistical learning methods were used to analyze structure in the set of operations applied to the time series, allowing us to relate different types of scientific methods to one another, and to investigate redundancy across them. An analogous process applied to the data allowed different types of time series to be linked based on their properties, and in particular to connect time series generated by theoretical models with those measured from relevant real-world systems. In the remainder of the thesis, methods for addressing specific problems in time-series analysis are presented that use our diverse collection of operations to represent time series in terms of their measured properties. The broad utility of this highly comparative approach is demonstrated using various case studies, including the discrimination of pathological heart beat series, classification of Parkinsonian phonemes, estimation of the scaling exponent of self-affine time series, prediction of cord pH from fetal heart rates recorded during labor, and the assignment of emotional content to speech recordings. Our methods are also applied to labeled datasets of short time-series patterns studied in temporal data mining, where our feature-based approach exhibits benefits over conventional time-domain classifiers. Lastly, a feature-based dimensionality reduction framework is developed that links dependencies measured between operations to the number of free parameters in a time-series model that could be used to generate a time-series dataset.

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Livres sur le sujet "Time-series analysis"

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Madsen, Henrik. Time series analysis. Boca Raton: Chapman & Hall/CRC, 2008.

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Ostrom, Charles. Time Series Analysis. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1990. http://dx.doi.org/10.4135/9781412986366.

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Tanaka, Katsuto. Time Series Analysis. Hoboken, New Jersey: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119132165.

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Cryer, Jonathan D., and Kung-Sik Chan. Time Series Analysis. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75959-3.

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Maurice, Kendall. Time series. 3rd ed. Sevenoaks: Edward Arnold, 1993.

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C, Harvey A., ed. Time series. Aldershot, Hants, England: E. Elgar, 1994.

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Shumway, Robert H. Applied statistical time series analysis. London: Prentice-Hall International, 1988.

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Hardin, Jay C. Introduction to time series analysis. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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Harvey, A. C. The econometric analysis of time series. 2nd ed. New York: P. Allen, 1990.

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Harvey, A. C. The econometric analysis of time series. Cambridge, Mass: MIT Press, 1989.

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Chapitres de livres sur le sujet "Time-series analysis"

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Tay, Dennis. "Time series analysis." In Data Analytics for Discourse Analysis with Python, 126–64. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003360292-5.

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Brandt, Siegmund. "Time Series Analysis." In Data Analysis, 331–40. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03762-2_13.

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Arkes, Jeremy. "Time-series models." In Regression Analysis, 287–314. 2nd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003285007-10.

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Brandt, Siegmund. "Time Series Analysis." In Data Analysis, 427–40. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1446-5_13.

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Myers, Sara A. "Time Series." In Nonlinear Analysis for Human Movement Variability, 29–53. Boca Raton : Taylor & Francis, Taylor & Francis, a CRC title, part of the: CRC Press, 2018. http://dx.doi.org/10.1201/9781315370651-2.

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Baltagi, Badi H. "Time-Series Analysis." In Solutions Manual for Econometrics, 341–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03383-4_14.

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Baltagi, Badi H. "Time-Series Analysis." In Econometrics, 363–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58714-6_14.

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Chatfield, Christopher. "Time-series analysis." In Problem Solving, 154–60. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-3017-0_19.

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Trauth, Martin H. "Time-Series Analysis." In MATLAB® Recipes for Earth Sciences, 151–213. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46244-7_5.

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Baltagi, Badi H. "Time-Series Analysis." In Springer Texts in Business and Economics, 383–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54548-1_14.

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Actes de conférences sur le sujet "Time-series analysis"

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Listiani, Indira Aprilia, Massimo Zanetti, and Francesca Bovolo. "Time Series Directional Change Vector Analysis." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 8683–86. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642170.

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Kurbalija, Vladimir, and Brankica Bratic. "Time series reconstruction analysis." In 2016 IEEE 8th International Conference on Intelligent Systems (IS). IEEE, 2016. http://dx.doi.org/10.1109/is.2016.7737400.

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KESLER, SB. "INTERPLAY BETWEEN TIME SERIES ANALYSIS AND SPATIAL SERIES ANALYSIS." In International Conference on Spectral Analysis and its Use in Underwater Acoustics 1982. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/23105.

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Müller, Ursula U., Anton Schick, and Wolfgang Wefelmeyer. "Inference for Alternating Time Series." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0069.

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Dvořák, Marek. "Time series convolution kernel estimation." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5044115.

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Mei, Xu, and Huang Chao. "Financial time series difference analysis based on symbolic time series method." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5882598.

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TARQUIS, ANA M., ROSA M. BENAVENTE, ANTONIO ROMERO, JOSÉ L. GARCÍA, and PHILIPPE BAVEYE. "WIND VELOCITY TIME SERIES ANALYSIS." In Conference on Fractals 2002. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777720_0040.

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Kawamae, Noriaki. "Time Series Analysis Using NOC." In the 25th International Conference Companion. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2872518.2889396.

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Muñoz-Diosdado, A. "Multifractal Analysis of Time Series." In MODELING OF COMPLEX SYSTEMS: Seventh Granada Lectures. AIP, 2003. http://dx.doi.org/10.1063/1.1571344.

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Corinaldi, Sharif, and Leon Cohen. "Time-frequency analysis of econometric time series." In SPIE Fourth International Symposium on Fluctuations and Noise, edited by János Kertész, Stefan Bornholdt, and Rosario N. Mantegna. SPIE, 2007. http://dx.doi.org/10.1117/12.726112.

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Rapports d'organisations sur le sujet "Time-series analysis"

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202273.

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161375.

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Lai, Eric, Daniel Moyer, Baichuan Yuan, Eric Fox, Blake Hunter, Andrea L. Bertozzi, and Jeffrey Brantingham. Topic Time Series Analysis of Microblogs. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada610278.

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Friedman, Avner, Jr Miller, and Willard. Radar/Sonar and Time Series Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 1991. http://dx.doi.org/10.21236/ada238496.

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Ricca, Bernard. Nonlinear Time Series Analysis (Part I). Instats Inc., 2024. https://doi.org/10.61700/3t1w239ur7tk61382.

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This intensive four-day workshop provides a comprehensive introduction to nonlinear time series analysis, focusing on state space reconstruction and identifying evidence of nonlinear dynamics, using the R software. Participants will develop skills to manipulate, visualize, and create nonlinear models of time series data, enhancing their research in any field that works with time series data.

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Lipsett, J. J., R. D. Noble, and D. D. S. Liu. Time series analysis of gamma densitometry signals. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/302665.

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Langdon, Chris. Analysis of Arabian Sea Oxygen Time Series. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada628003.

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Lewis, Peter A., and A. J. Lawrance. Reversed Residuals in Autoregressive Time Series Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada222711.

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Parzen, Emanuel. Stationary Time Series Analysis Using Information and Spectral Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada257279.

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Wheat, Jr., Robert M. Chaos in Electronic Circuits: Nonlinear Time Series Analysis. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/821547.

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