Literatura académica sobre el tema "Time series"
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Artículos de revistas sobre el tema "Time series"
Cipra, Tomáš. "Asymmetric recursive methods for time series". Applications of Mathematics 39, n.º 3 (1994): 203–14. http://dx.doi.org/10.21136/am.1994.134253.
Texto completoRatinger, Tomáš. "Seasonal time series with missing observations". Applications of Mathematics 41, n.º 1 (1996): 41–55. http://dx.doi.org/10.21136/am.1996.134312.
Texto completoCIUIU, Daniel. "STRICT STATIONARY TIME SERIES AND AUTOCOPULA". Review of the Air Force Academy 16, n.º 2 (31 de octubre de 2018): 53–58. http://dx.doi.org/10.19062/1842-9238.2018.16.2.6.
Texto completoRay, W. D., Maurice Kendall y J. K. Ord. "Time Series." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, n.º 2 (1994): 308. http://dx.doi.org/10.2307/2983371.
Texto completoBooth, David E., Maurice Kendall y J. Keith Ord. "Time Series". Technometrics 34, n.º 1 (febrero de 1992): 118. http://dx.doi.org/10.2307/1269585.
Texto completoKK, Maurice Kendall y J. Keith Ord. "Time Series." Journal of the American Statistical Association 90, n.º 432 (diciembre de 1995): 1492. http://dx.doi.org/10.2307/2291552.
Texto completoKK y Andrew Harvey. "Time Series." Journal of the American Statistical Association 90, n.º 432 (diciembre de 1995): 1493. http://dx.doi.org/10.2307/2291556.
Texto completoZiegel, Eric R. "Time Series". Technometrics 44, n.º 4 (noviembre de 2002): 408. http://dx.doi.org/10.1198/tech.2002.s95.
Texto completoHolmes, William M. "Time Series". International Journal of Forecasting 7, n.º 4 (marzo de 1992): 532–33. http://dx.doi.org/10.1016/0169-2070(92)90037-a.
Texto completoLounds, W. S., M. Kendall y J. K. Ord. "Time Series." Statistician 43, n.º 3 (1994): 461. http://dx.doi.org/10.2307/2348592.
Texto completoTesis sobre el tema "Time series"
Rajan, Jebu Jacob. "Time series classification". Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339538.
Texto completoPope, Kenneth James. "Time series analysis". Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318445.
Texto completoYin, Jiang Ling. "Financial time series analysis". Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.
Texto completoGore, 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.
Texto completoVita. 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).
NETO, ANSELMO CHAVES. "BOOTSTRAP IN TIME SERIES". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8324@1.
Texto completoThe bootstrap of B. Efron, what should not be imagined without fast andcheaper computation, can solve several problems free from assumption that the data conform to a bell-shaped curve. This work has the aim to present this computer-intensive technics in the context of Time Series - Box and Jenkins´s Methodology. As we know this methodology own some asymptotic results. Then in the identification stage of the structure of the model it may present some troubles on regions of the parametric space, as we show later on the bootstrap is proposed as an aption and a comparative simulation study is pointed out. We build up the bootstrap distribution of the sample autocorrelation and sample partial autocorrelation, and yet a bootstrap distribution to the non-linear LS estimator of the coefficients to the ARMA (p,q) model. As a consequence we get the non- parametric measure of the accuracy of the estimates. The study of simulation wich takes into account the non-linear LS estimato to the coefficients, actually focalize the borden of the stationarity and invertibility region.
AGUIAR, JOSE LUIZ DO NASCIMENTO DE. "TIME SERIES SYMILARITY MEASURES". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27789@1.
Texto completoCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Atualmente, uma tarefa muito importante na mineração de dados é compreender como extrair os dados mais informativos dentre um número muito grande de dados. Uma vez que todos os campos de conhecimento apresentam uma grande quantidade de dados que precisam ser reduzidas até as informações mais representativas, a abordagem das séries temporais é definitivamente um método muito forte para representar e extrair estas informações. No entanto nós precisamos ter uma ferramenta apropriada para inferir os dados mais significativos destas séries temporais, e para nos ajudar, podemos utilizar alguns métodos de medida de similaridade para saber o grau de igualdade entre duas séries temporais, e nesta pesquisa nós vamos realizar um estudo utilizando alguns métodos de similaridade baseados em medidas de distância e aplicar estes métodos em alguns algoritmos de clusterização para fazer uma avaliação de se existe uma combinação (método de similaridade baseado em distância / algoritmo de clusterização) que apresenta uma performance melhor em relação a todos os outros utilizados neste estudo, ou se existe um método de similaridade baseado em distância que mostra um desempenho melhor que os demais.
Nowadays a very important task in data mining is to understand how to collect the most informative data in a very amount of data. Once every single field of knowledge have lots of data to summarize in the most representative information, the time series approach is definitely a very strong way to represent and collect this information from it (12, 22). On other hand we need to have an appropriate tool to extract the most significant data from this time series. To help us we can use some similarity methods to know how similar is one time series from another In this work we will perform a research using some distance-based similarity methods and apply it in some clustering algorithms to do an assessment to see if there is a combination (distance-based similarity methods / clustering algorithm) that present a better performance in relation with all the others used in this work or if there exists one distancebased similarity method that shows a better performance between the others.
Yin, Yong. "Outliers in Time Series /". Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262638388.
Texto completoRana, Md Mashud. "Energy time series prediction". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11745.
Texto completoGrubb, Howard John. "Multivariate time series modelling". Thesis, University of Bath, 1990. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280803.
Texto completoAhsan, Ramoza. "Time Series Data Analytics". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.
Texto completoLibros sobre el tema "Time series"
Kendall, Maurice G. Time series. 3a ed. Seven Oaks, Kent: E. Arnold, 1990.
Buscar texto completoMaurice, Kendall. Time series. 3a ed. Sevenoaks: Edward Arnold, 1993.
Buscar texto completoC, Harvey A., ed. Time series. Aldershot, Hants, England: E. Elgar, 1994.
Buscar texto completoChan, Ngai Hang. Time Series. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9781118032466.
Texto completoPractical time series. London: Arnold, 2001.
Buscar texto completoMadsen, Henrik. Time series analysis. Boca Raton: Chapman & Hall/CRC, 2008.
Buscar texto completoTime series analysis. Hoboken: John Wiley & Sons, Inc., 2016.
Buscar texto completoTime Series Analysis. Princeton, NJ, USA: Princeton University Press, 1994.
Buscar texto completoTime-series forecasting. Boca Raton: Chapman & Hall/CRC, 2001.
Buscar texto completoOstrom, 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.
Texto completoCapítulos de libros sobre el tema "Time series"
Marin, Jean-Michel y Christian P. Robert. "Time Series". En Springer Texts in Statistics, 209–50. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8687-9_7.
Texto completoVenables, W. N. y B. D. Ripley. "Time Series". En Modern Applied Statistics with S-Plus, 349–82. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4899-2819-1_14.
Texto completoManly, Bryan F. J. "Time series". En Randomization and Monte Carlo Methods in Biology, 164–204. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2995-2_9.
Texto completoKass, Robert E., Uri T. Eden y Emery N. Brown. "Time Series". En Springer Series in Statistics, 513–61. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9602-1_18.
Texto completoVenables, W. N. y B. D. Ripley. "Time Series". En Statistics and Computing, 431–68. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2719-7_15.
Texto completoRouan, Daniel. "Time Series". En Encyclopedia of Astrobiology, 1675. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_1593.
Texto completoGooch, Jan W. "Time Series". En Encyclopedic Dictionary of Polymers, 999. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15403.
Texto completoHull, Isaiah. "Time Series". En Machine Learning for Economics and Finance in TensorFlow 2, 249–79. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6373-0_7.
Texto completoMukhopadhyay, Sayan. "Time Series". En Advanced Data Analytics Using Python, 121–43. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3450-1_6.
Texto completoKenny, Peter. "Time Series". En Better Business Decisions from Data, 197–203. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0184-8_19.
Texto completoActas de conferencias sobre el tema "Time series"
Gunopulos, Dimitrios y Gautam Das. "Time series similarity measures and time series indexing". En the 2001 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375663.375808.
Texto completode Carvalho, Osmar Abilio, Renato Fontes Guimaraes, Roberto Arnaldo Trancoso Gomes y Nilton Correia da Silva. "Time series interpolation". En 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423211.
Texto completoYe, Lexiang y Eamonn Keogh. "Time series shapelets". En the 15th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1557019.1557122.
Texto completoFrancke, Marc. "Time Series Econometrics". En 25th Annual European Real Estate Society Conference. European Real Estate Society, 2016. http://dx.doi.org/10.15396/eres2016_380.
Texto completoCombes, Florian, Ricardo Fraiman y Badih Ghattas. "Time Series Sampling". En ITISE 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/engproc2022018032.
Texto completoYe, Yufeng, Qichao He, Peng Zhang, Jie Xiao y Zhao Li. "Multivariate Time Series Anomaly Detection with Fourier Time Series Transformer". En 2023 IEEE 12th International Conference on Cloud Networking (CloudNet). IEEE, 2023. http://dx.doi.org/10.1109/cloudnet59005.2023.10490086.
Texto completoRakthanmanon, Thanawin, Eamonn J. Keogh, Stefano Lonardi y Scott Evans. "Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data". En 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE, 2011. http://dx.doi.org/10.1109/icdm.2011.146.
Texto completoZhu, Yan, Makoto Imamura, Daniel Nikovski y Eamonn Keogh. "Time Series Chains: A Novel Tool for Time Series Data Mining". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/764.
Texto completoMei, Xu y Huang Chao. "Financial time series difference analysis based on symbolic time series method". En 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5882598.
Texto completoKurbalija, Vladimir y Brankica Bratic. "Time series reconstruction analysis". En 2016 IEEE 8th International Conference on Intelligent Systems (IS). IEEE, 2016. http://dx.doi.org/10.1109/is.2016.7737400.
Texto completoInformes sobre el tema "Time series"
Broemeling, Lyle. Changing Time Series. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1986. http://dx.doi.org/10.21236/ada178030.
Texto completoAdler, Robert J., Raisa E. Feldman y Colin Gallagher. Analysing Stable Time Series. Fort Belvoir, VA: Defense Technical Information Center, enero de 1997. http://dx.doi.org/10.21236/ada336964.
Texto completoRamos, Ernesto. Resampling Methods for Time Series. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1988. http://dx.doi.org/10.21236/ada198942.
Texto completoWilliamson, M. A. Benchmarking of energy time series. Office of Scientific and Technical Information (OSTI), abril de 1990. http://dx.doi.org/10.2172/6990845.
Texto completoTichomirova, T. M. y A. G. Sukiasyan. Test bank «Time Series Models». Ailamazyan Program Systems Institute of Russian Academy of Sciences, noviembre de 2023. http://dx.doi.org/10.12731/ofernio.2023.25215.
Texto completoLai, Eric, Daniel Moyer, Baichuan Yuan, Eric Fox, Blake Hunter, Andrea L. Bertozzi y Jeffrey Brantingham. Topic Time Series Analysis of Microblogs. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2014. http://dx.doi.org/10.21236/ada610278.
Texto completoGlynn, P. W. y D. L. Iglehart. The Theory of Standardized Time Series. Fort Belvoir, VA: Defense Technical Information Center, abril de 1985. http://dx.doi.org/10.21236/ada158383.
Texto completoFriedman, Avner, Jr Miller y Willard. Radar/Sonar and Time Series Analysis. Fort Belvoir, VA: Defense Technical Information Center, abril de 1991. http://dx.doi.org/10.21236/ada238496.
Texto completoJääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, septiembre de 2023. http://dx.doi.org/10.35614/isbn.9789523361782.
Texto completoDeSmet, Chance, Michael Girard, Elizabeth Coda y Yijing Watkins. Universal Fourier Attack for Time Series. Office of Scientific and Technical Information (OSTI), septiembre de 2023. http://dx.doi.org/10.2172/2338170.
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