Relevant bibliographies by topics / Long time series

Contents

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

Academic literature on the topic 'Long time series'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Long time series"

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Huang, Guangdong, and Jiahong Li. "Hybrid Time Series Method for Long-Time Temperature Series Analysis." Discrete Dynamics in Nature and Society 2021 (July 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/9968022.

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This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algo
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Gao, Jiti, Qiying Wang, and Jiying Yin. "Long-range dependent time series specification." Bernoulli 19, no. 5A (2013): 1714–49. http://dx.doi.org/10.3150/12-bej427.

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Souza, Leonardo Rocha. "Why Aggregate Long Memory Time Series?" Econometric Reviews 27, no. 1-3 (2008): 298–316. http://dx.doi.org/10.1080/07474930701873408.

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Davidson, James, and Timo Teräsvirta. "Long memory and nonlinear time series." Journal of Econometrics 110, no. 2 (2002): 105–12. http://dx.doi.org/10.1016/s0304-4076(02)00088-x.

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Godfray, H. C. J., and M. P. Hassell. "Long time series reveal density dependence." Nature 359, no. 6397 (1992): 673–74. http://dx.doi.org/10.1038/359673a0.

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Li, Degui, Peter M. Robinson, and Han Lin Shang. "Long-Range Dependent Curve Time Series." Journal of the American Statistical Association 115, no. 530 (2019): 957–71. http://dx.doi.org/10.1080/01621459.2019.1604362.

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Imani, Shima, Frank Madrid, Wei Ding, Scott E. Crouter, and Eamonn Keogh. "Introducing time series snippets: a new primitive for summarizing long time series." Data Mining and Knowledge Discovery 34, no. 6 (2020): 1713–43. http://dx.doi.org/10.1007/s10618-020-00702-y.

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Menzel, Annette, and Volker Dose. "Analysis of long-term time series of the beginning of flowering by Bayesian function estimation." Meteorologische Zeitschrift 14, no. 3 (2005): 429–34. http://dx.doi.org/10.1127/0941-2948/2005/0040.

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McClatchie, Sam, Janet Duffy-Anderson, John Field, et al. "Long Time Series in US Fisheries Oceanography." Oceanography 27, no. 4 (2014): 48–67. http://dx.doi.org/10.5670/oceanog.2014.86.

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YAJIMA, Yoshihiro. "Long-memory Models in Time Series Analysis." Japanese journal of applied statistics 23, no. 1 (1994): 1–19. http://dx.doi.org/10.5023/jappstat.23.1.

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Dissertations / Theses on the topic "Long time series"

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Song, Li. "Piecewise models for long memory time series." Paris 11, 2010. http://www.theses.fr/2010PA112128.

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De nombreux travaux existent sur les processus stationnaires à longue mémoire (LM) et sur les modèles présentant des changements structurels. Cependant, il y a relativement peu de travaux sur les processus à LM non stationnaires faisant intervenir des ruptures, et ceci sans doute car LM et changement structurel sont deux phénomènes qu'il est facile de confondre. Dans cette thèse, nous considérons un modèle paramétrique de séries chronologiques à LM et non stationnaires : le processus localement autorégressif à moyenne mobile fractionnairement intégrée (FARlMA). Dans ce modèle, le nombre et les
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Thyer, Mark Andrew. "Modelling long-term persistence in hydrological time series." Diss., 2000, 2000. http://www.newcastle.edu.au/services/library/adt/public/adt-NNCU20020531.035349/index.html.

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Arteche, Jesus Maria. "Seasonal and cyclical long-memory in time series." Thesis, London School of Economics and Political Science (University of London), 1998. http://etheses.lse.ac.uk/2077/.

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This thesis deals with the issue of persistence, focusing on economic time series, and extending the subject to seasonal and cyclical long memory time series. Such processes are defined. In the frequency domain they are characterized by spectral poles/zeros at some frequency ω between 0 and π. First we review some of the work done to date on seasonality and long memory, and we focus on research that try to link both issues. One of the limitations of the existing work is the imposition of asymptotic symmetry in the spectral density around ω. We describe some processes that allow for spectral as
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Fraser, William Stuart Alistair. "An analysis of long memory vector time series." Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389715.

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Algarhi, Amr Saber Ibrahim. "Essays on long memory time series and fractional cointegration." Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/13791.

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The dissertation considers an indirect approach for the estimation of the cointegrating parameters, in the sense that the estimators are jointly constructed along with estimating other nuisance parameters. This approach was proposed by Robinson (2008) where a bivariate local Whittle estimator was developed to jointly estimate a cointegrating parameter along with the memory parameters and the phase parameters (discussed in chapter 2). The main contributions of this dissertation is to establish, similar to Robinson (2008), a joint estimation of the memory, cointegrating and phase parameters in s
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Lazarova, Stepana. "Long memory and structural breaks in time series models." Thesis, London School of Economics and Political Science (University of London), 2006. http://etheses.lse.ac.uk/1927/.

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This thesis examines structural breaks in time series regressions where both regressors and errors may exhibit long range dependence. Statistical properties of methods for detecting and estimating structural breaks are analysed and asymptotic distribution of estimators and test statistics are obtained. Valid bootstrap methods of approximating the limiting distribution of the relevant statistics are also developed to improve on the asymptotic approximation in finite samples or to deal with the problem of unknown asymptotic distribution. The performance of the asymptotic and bootstrap methods ar
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Leschinski, Christian Hendrik [Verfasser]. "Essays on long memory time series / Christian Hendrik Leschinski." Hannover : Technische Informationsbibliothek (TIB), 2016. http://d-nb.info/1112948805/34.

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Jaunzems, Davis. "Time-series long-term forcasting for A/B tests." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205344.

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Den tekniska utvecklingen av datorenheter och kommunikationsverktyg har skapat möjligheter att lagra och bearbeta större mängder information än någonsin tidigare. För forskare är det ett sätt att göra mer exakta vetenskapliga upptäckter, för företag är det ett verktyg för att bättre förstå sina kunder, sina produkter och att skapa fördelar gentemot sina konkurrenter. Inom industrin har A/B-testning blivit ett viktigt och vedertaget sätt att skaffa kunskaper som bidrar till att kunna fatta datadrivna beslut. A/B-test är en jämförelse av två eller flera versioner för att avgöra vilken som funger
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Andersson, Michael K. "On testing and forecasting in fractionally integrated time series models." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1998. http://www.hhs.se/efi/summary/467.htm.

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Ellis, Craig, of Western Sydney Macarthur University, and Faculty of Business and Technology. "An investigation of long-term dependence in time-series data." THESIS_FBT_XXX_Ellis_C.xml, 1998. http://handle.uws.edu.au:8081/1959.7/242.

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Traditional models of financial asset yields are based on a number of simplifying assumptions. Among these are the primary assumptions that changes in asset yields are independent, and that the distribution of these yields is approximately normal. The development of financial asset pricing models has also incorporated these assumptions. A general feature of the pricing models is that the relationship between the model variables is fundamentally linear. Recent empirical research has however identified the possibility for these relations to be non-linear. The empirical research focused primarily
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Books on the topic "Long time series"

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Palma, Wilfredo. Long-Memory Time Series. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470131466.

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Reisen, V. A. Long memory time series models. UMIST, 1993.

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Long-memory time series: Theory and methods. Wiley-Interscience, 2007.

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Teyssiere, Gilles. Double long-memory financial time series. London University, Queen Mary and Westfield College, Department of Economics, 1996.

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Robinson, P. M. Time series regression with long range dependence. Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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León, Javier. Structural breaks and long-run trends in commodity prices. World Bank, 1995.

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Robinson, P. M. Modelling nonlinearity and long memory in time series. Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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Chambers, Marcus J. Long memory and aggregation in macroeconomic time series. Essex University, Department of Economics, 1995.

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Hassler, Uwe. Time Series Analysis with Long Memory in View. John Wiley & Sons, Inc, 2018. http://dx.doi.org/10.1002/9781119470380.

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Koop, Gary. Bayesian long-run prediction in time series models. Cracow Academy of Economics, 1992.

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Book chapters on the topic "Long time series"

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "Long Memory Time Series." In Universitext. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54539-9_14.

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "Long Memory Time Series." In Statistics of Financial Markets. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16521-4_14.

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "Long Memory Time Series." In Universitext. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13751-9_14.

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McDowell, Patricia F., Thompson Webb, and Patrick J. Bartlein. "Long-Term Environmental Change." In Ecological Time Series. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-6881-0_16.

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McDowell, Patricia F., Thompson Webb, and Patrick J. Bartlein. "Long-Term Environmental Change." In Ecological Time Series. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1769-6_16.

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Kulik, Rafał, and Philippe Soulier. "Long memory processes." In Heavy-Tailed Time Series. Springer New York, 2020. http://dx.doi.org/10.1007/978-1-0716-0737-4_16.

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Zivot, Eric, and Jiahui Wang. "Long Memory Time Series Modeling." In Modeling Financial Time Series with S-Plus®. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21763-5_8.

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Cowpertwait, Paul S. P., and Andrew V. Metcalfe. "Long-Memory Processes." In Introductory Time Series with R. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-88698-5_8.

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Doukhan, Paul. "Long-Range Dependence." In Stochastic Models for Time Series. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76938-7_10.

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Robinson, P. M. "Long memory models." In Macroeconometrics and Time Series Analysis. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_19.

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Conference papers on the topic "Long time series"

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Hong Zhang, Zhimin Liu, and Keqiang Dong. "The long-term correlation of conditional time series of traffic time series." In 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405895.

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Zhang, Hong, Haikun Zhou, Zhimin Liu, and Keqiang Dong. "The Long-term Correlation of Conditional Time Series of Financial Time Series." In 2009 Third International Symposium on Intelligent Information Technology Application. IEEE, 2009. http://dx.doi.org/10.1109/iita.2009.124.

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Zhou, Yi, Yu Shang, Jiangyun Li, and Qiufeng Tang. "Stochastic Long Time Series Rainfall Generation Method." In International Low Impact Development Conference China 2016. American Society of Civil Engineers, 2017. http://dx.doi.org/10.1061/9780784481042.011.

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Yan, Weizhong, Hai Qiu, and Ya Xue. "Gaussian process for long-term time-series forecasting." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178729.

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Khayati, Mourad, Michael Bohlen, and Johann Gamper. "Memory-efficient centroid decomposition for long time series." In 2014 IEEE 30th International Conference on Data Engineering (ICDE). IEEE, 2014. http://dx.doi.org/10.1109/icde.2014.6816643.

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Hirano, Shoji, and Shusaku Tsumoto. "Cluster analysis of long time-series medical datasets." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.542931.

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Fujimoto, Kazuki, Sho Sakaino, and Toshiaki Tsuji. "Time Series Motion Generation Considering Long Short-Term Motion." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968587.

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Mendoza, Marcelo, Barbara Poblete, Felipe Bravo-Marquez, and Daniel Gayo-Avello. "Long-memory time series ensembles for concept shift detection." In the 2nd International Workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2501221.2501225.

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Huang, Zifang, Mei-Ling Shyu, and James M. Tien. "Multi-model integration for long-term time series prediction." In 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI). IEEE, 2012. http://dx.doi.org/10.1109/iri.2012.6302999.

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Wu, Jiaye, Yang Wang, Peng Wang, Jian Pei, and Wei Wang. "Finding Maximal Significant Linear Representation between Long Time Series." In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00179.

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Reports on the topic "Long time series"

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Downing, D. J., W. F. Lawkins, M. D. Morris, and G. Ostrouchov. A method for detecting changes in long time series. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/161513.

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Chen, Z., and S. E. Grasby. Temporal trend analysis of synthetic and real hydroclimate time series and impacts of long term quasi-periodic components on trend tests. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2009. http://dx.doi.org/10.4095/247832.

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Wiener, Joshua M., Mary E. Knowles, and Erin E. White. Financing Long-Term Services and Supports: Continuity and Change. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0042.1709.

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This article provides an overview of financing for long-term services and supports (LTSS) in the United States, paying special attention to how it has changed and not changed over the last 30 years. Although LTSS expenditures have increased greatly (like the rest of health care), the broad outline of the financing system has remained remarkably constant. Medicaid—a means-tested program—continues to dominate LTSS financing, while private long-term care insurance plays a minor role. High out-of-pocket costs and spend-down to Medicaid because of those high costs continue to be hallmarks of the sy
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Day, St John, Tim Forster, and Ryan Schweitzer. Water Supply in Protracted Humanitarian Crises: Reflections on the sustainability of service delivery models. Oxfam, UNHCR, 2020. http://dx.doi.org/10.21201/2020.6362.

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UNHCR estimates that the average time spent by a refugee in a camp is 10 years, while the average refugee camp remains for 26 years. WASH (water, sanitation and hygiene) is a crucial component of humanitarian response and longer-term recovery. Humanitarian agencies and host governments face many challenges in protracted situations and complex long-term humanitarian crises. One key issue is how water supplies should be managed in the long term. Who is best placed to operate and manage WASH services and which delivery model is the most viable? At the end of 2019, there were 15.7 million refugees
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Johnson, Eric M., Robert Urquhart, and Maggie O'Neil. The Importance of Geospatial Data to Labor Market Information. RTI Press, 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0017.1806.

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School-to-work transition data are an important component of labor market information systems (LMIS). Policy makers, researchers, and education providers benefit from knowing how long it takes work-seekers to find employment, how and where they search for employment, the quality of employment obtained, and how steady it is over time. In less-developed countries, these data are poorly collected, or not collected at all, a situation the International Labour Organization and other donors have attempted to change. However, LMIS reform efforts typically miss a critical part of the picture—the geosp
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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting
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Carrasquilla-Barrera, Alberto, Arturo José Galindo-Andrade, Gerardo Hernández-Correa, et al. Report of the Board of Directors to the Congress of Colombia - July 2020. Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/inf-jun-dir-con-rep-eng.07-2020.

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In Colombia, as well as in the rest of the world, the Covid-19 pandemic has seriously damaged the health and well-being of the people. In order to limit the damage, local and national authorities have had to order large sectors of the population to be confined at their homes for long periods of time. An inevitable consequence of isolation has been the collapse of economic activity, expenditure, and employment, a phenomenon that has hit many countries of the world affected by the disease. It is an unprecedented crisis in modern times, not so much for its intensity (which is undoubtedly immense)
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Syvash, Kateryna. AUDIENCE FEEDBACK AS AN ELEMENT OF PARASOCIAL COMMUNICATION WITH SCREEN MEDIA-PERSONS. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.49.11062.

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Parasocial communication is defined as an illusory and one-sided interaction between the viewer and the media person, which is analogous to interpersonal communication. Among the classic media, television has the greatest potential for such interaction through a combination of audio and visual series and a wide range of television content – from newscasts to talent shows. Viewers’ reaction to this product can be seen as a defining element of parasociality and directly affect the popularity of a media person and the ratings of the TV channel. In this article we will consider feedback as part of
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Nolan, Brian, Brenda Gannon, Richard Layte, Dorothy Watson, Christopher T. Whelan, and James Williams. Monitoring Poverty Trends in Ireland: Results from the 2000 Living in Ireland survey. ESRI, 2002. http://dx.doi.org/10.26504/prs45.

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This study is the latest in a series monitoring the evolution of poverty, based on data gathered by The ESRI in the Living in Ireland Surveys since 1994. These have allowed progress towards achieving the targets set out in the National Anti Poverty Strategy since 1997 to be assessed. The present study provides an updated picture using results from the 2000 round of the Living in Ireland survey. The numbers interviewed in the 2000 Living in Ireland survey were enhanced substantially, to compensate for attrition in the panel survey since it commenced in 1994. Individual interviews were conducted
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Vargas-Herrera, Hernando, Juan Jose Ospina-Tejeiro, Carlos Alfonso Huertas-Campos, et al. Monetary Policy Report - April de 2021. Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr2-2021.

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1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lowe
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