Relevant bibliographies by topics / Time series

Contents

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

Academic literature on the topic 'Time series'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 October 2024

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Journal articles on the topic "Time series"

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Cipra, Tomáš. "Asymmetric recursive methods for time series." Applications of Mathematics 39, no. 3 (1994): 203–14. http://dx.doi.org/10.21136/am.1994.134253.

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Ratinger, Tomáš. "Seasonal time series with missing observations." Applications of Mathematics 41, no. 1 (1996): 41–55. http://dx.doi.org/10.21136/am.1996.134312.

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CIUIU, Daniel. "STRICT STATIONARY TIME SERIES AND AUTOCOPULA." Review of the Air Force Academy 16, no. 2 (October 31, 2018): 53–58. http://dx.doi.org/10.19062/1842-9238.2018.16.2.6.

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Ray, W. D., Maurice Kendall, and J. K. Ord. "Time Series." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 2 (1994): 308. http://dx.doi.org/10.2307/2983371.

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Booth, David E., Maurice Kendall, and J. Keith Ord. "Time Series." Technometrics 34, no. 1 (February 1992): 118. http://dx.doi.org/10.2307/1269585.

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KK, Maurice Kendall, and J. Keith Ord. "Time Series." Journal of the American Statistical Association 90, no. 432 (December 1995): 1492. http://dx.doi.org/10.2307/2291552.

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KK and Andrew Harvey. "Time Series." Journal of the American Statistical Association 90, no. 432 (December 1995): 1493. http://dx.doi.org/10.2307/2291556.

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Ziegel, Eric R. "Time Series." Technometrics 44, no. 4 (November 2002): 408. http://dx.doi.org/10.1198/tech.2002.s95.

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Holmes, William M. "Time Series." International Journal of Forecasting 7, no. 4 (March 1992): 532–33. http://dx.doi.org/10.1016/0169-2070(92)90037-a.

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Lounds, W. S., M. Kendall, and J. K. Ord. "Time Series." Statistician 43, no. 3 (1994): 461. http://dx.doi.org/10.2307/2348592.

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Dissertations / Theses on the topic "Time series"

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Rajan, Jebu Jacob. "Time series classification." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339538.

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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.
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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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.

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O bootstrap de B. Efron, que não poderia ser imaginado sem os computadores de hoje, pode resolver vários problemas livre da suposição de Gaussianidade para os dados. Este trabalho tem o objetivo de apresentar essa técnica computacionalmente intensiva no contexto de Séries temporais - Metodologia Box and Jenkins. Como se sabe essa Metodologia possui alguns resultados assintóticos. Então, na fase da identificação da estrutura do modelo, pode apresentar problemas em regiões do espaço paramétrico aqui determinadas,. O bootstrap é proposto como opção e um estudo de simulação, comparativo, é apresentado. Constrói- se a distribuição bootstrap da autocorrelação e autocorrelação parcial, amostrais, e ainda a distribuição bootstrap do estimador de MQNL dos coeficientes de modelos ARMA (p, q). consequentemente, fica disponí­vel medida não- paramétrica da precisão da estimativa. O estudo de simulação que aborda o estimador de MQNL dos coeficientes enfoca, basicamente, a região de fronteira da estacionariedade e inversibilidade.
The 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.
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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.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO 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.
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Yin, Yong. "Outliers in Time Series /." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262638388.

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Rana, Md Mashud. "Energy time series prediction." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11745.

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Reliable operations and economical utilization of power systems require electricity load forecasting at a wide range of forecasting horizons. The objective of this thesis is two-fold: developing accurate prediction models for electricity load forecasting, and quantifying the load forecasting uncertainty. At first, we consider the task of feature selection for electricity load forecasting. We propose a two-step approach - identifying a set of candidate features based on the data characteristics and then selecting a subset of them using four different methods. We evaluate the performance of these methods using state-of-the-art prediction algorithms. The results show that all feature selection methods are able to identify small subsets of highly relevant features for electricity load forecasting. We then present a generic approach for very short term electricity load forecasting. It combines multilevel wavelet packet transform, a non-linear feature selection method based on mutual information, and machine learning prediction algorithms. The evaluation shows that the proposed approach is robust and outperforms several non-wavelet based approaches. We also propose a novel approach for forecasting the daily load profile. The proposed approach uses mutual information for feature selection and an ensemble of neural networks for building a prediction model. The evaluation using two years of electricity load data for Australia, Portugal and Spain shows that it provides accurate predictions. Finally, we present LUBEX, a neural networks based approach for forecasting prediction intervals to quantify the uncertainty associated with electricity load prediction. LUBEX extends an existing method (LUBE) by including an advanced feature selection method and using an ensemble of neural networks. A comprehensive evaluation using 24 different case studies shows that LUBEX is able to generate high quality prediction intervals.
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Grubb, Howard John. "Multivariate time series modelling." Thesis, University of Bath, 1990. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280803.

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Ahsan, Ramoza. "Time Series Data Analytics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.

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Given the ubiquity of time series data, and the exponential growth of databases, there has recently been an explosion of interest in time series data mining. Finding similar trends and patterns among time series data is critical for many applications ranging from financial planning, weather forecasting, stock analysis to policy making. With time series being high-dimensional objects, detection of similar trends especially at the granularity of subsequences or among time series of different lengths and temporal misalignments incurs prohibitively high computation costs. Finding trends using non-metric correlation measures further compounds the complexity, as traditional pruning techniques cannot be directly applied. My dissertation addresses these challenges while meeting the need to achieve near real-time responsiveness. First, for retrieving exact similarity results using Lp-norm distances, we design a two-layered time series index for subsequence matching. Time series relationships are compactly organized in a directed acyclic graph embedded with similarity vectors capturing subsequence similarities. Powerful pruning strategies leveraging the graph structure greatly reduce the number of time series as well as subsequence comparisons, resulting in a several order of magnitude speed-up. Second, to support a rich diversity of correlation analytics operations, we compress time series into Euclidean-based clusters augmented by a compact overlay graph encoding correlation relationships. Such a framework supports a rich variety of operations including retrieving positive or negative correlations, self correlations and finding groups of correlated sequences. Third, to support flexible similarity specification using computationally expensive warped distance like Dynamic Time Warping we design data reduction strategies leveraging the inexpensive Euclidean distance with subsequent time warped matching on the reduced data. This facilitates the comparison of sequences of different lengths and with flexible alignment still within a few seconds of response time. Comprehensive experimental studies using real-world and synthetic datasets demonstrate the efficiency, effectiveness and quality of the results achieved by our proposed techniques as compared to the state-of-the-art methods.
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Books on the topic "Time series"

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Kendall, Maurice G. Time series. 3rd ed. Seven Oaks, Kent: E. Arnold, 1990.

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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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Chan, Ngai Hang. Time Series. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9781118032466.

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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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Deistler, Manfred, and Wolfgang Scherrer. Time Series Models. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1.

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Maoz, Dan, Amiel Sternberg, and Elia M. Leibowitz, eds. Astronomical Time Series. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8941-3.

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Powell, Thomas M., and John H. Steele, eds. Ecological Time Series. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-6881-0.

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Powell, Thomas M., and John H. Steele, eds. Ecological Time Series. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1769-6.

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Book chapters on the topic "Time series"

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Marin, Jean-Michel, and Christian P. Robert. "Time Series." In 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.

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Venables, W. N., and B. D. Ripley. "Time Series." In 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.

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Manly, Bryan F. J. "Time series." In 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.

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Kass, Robert E., Uri T. Eden, and Emery N. Brown. "Time Series." In 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.

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Venables, W. N., and B. D. Ripley. "Time Series." In Statistics and Computing, 431–68. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2719-7_15.

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Rouan, Daniel. "Time Series." In Encyclopedia of Astrobiology, 1675. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_1593.

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Gooch, Jan W. "Time Series." In Encyclopedic Dictionary of Polymers, 999. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15403.

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Hull, Isaiah. "Time Series." In 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.

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Mukhopadhyay, Sayan. "Time Series." In Advanced Data Analytics Using Python, 121–43. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3450-1_6.

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Kenny, Peter. "Time Series." In Better Business Decisions from Data, 197–203. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0184-8_19.

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Conference papers on the topic "Time series"

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Østergaard, Jan. "Directed Redundancy in Time Series." In 2024 IEEE International Symposium on Information Theory (ISIT), 1973–78. IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619644.

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Gunopulos, Dimitrios, and Gautam Das. "Time series similarity measures and time series indexing." In the 2001 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375663.375808.

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de Carvalho, Osmar Abilio, Renato Fontes Guimaraes, Roberto Arnaldo Trancoso Gomes, and Nilton Correia da Silva. "Time series interpolation." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423211.

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Ye, Lexiang, and Eamonn Keogh. "Time series shapelets." In the 15th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1557019.1557122.

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Francke, Marc. "Time Series Econometrics." In 25th Annual European Real Estate Society Conference. European Real Estate Society, 2016. http://dx.doi.org/10.15396/eres2016_380.

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Combes, Florian, Ricardo Fraiman, and Badih Ghattas. "Time Series Sampling." In ITISE 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/engproc2022018032.

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Ye, Yufeng, Qichao He, Peng Zhang, Jie Xiao, and Zhao Li. "Multivariate Time Series Anomaly Detection with Fourier Time Series Transformer." In 2023 IEEE 12th International Conference on Cloud Networking (CloudNet). IEEE, 2023. http://dx.doi.org/10.1109/cloudnet59005.2023.10490086.

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Rakthanmanon, Thanawin, Eamonn J. Keogh, Stefano Lonardi, and Scott Evans. "Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data." In 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE, 2011. http://dx.doi.org/10.1109/icdm.2011.146.

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Zhu, Yan, Makoto Imamura, Daniel Nikovski, and Eamonn Keogh. "Time Series Chains: A Novel Tool for Time Series Data Mining." In 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.

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Since their introduction over a decade ago, time se-ries motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbi-trarily dissimilar. In the discrete space, this is simi-lar to extracting the text chain “hit, hot, dot, dog” from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time Series Chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets.
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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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Reports on the topic "Time series"

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Broemeling, Lyle. Changing Time Series. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada178030.

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Adler, Robert J., Raisa E. Feldman, and Colin Gallagher. Analysing Stable Time Series. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada336964.

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Ramos, Ernesto. Resampling Methods for Time Series. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada198942.

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Williamson, M. A. Benchmarking of energy time series. Office of Scientific and Technical Information (OSTI), April 1990. http://dx.doi.org/10.2172/6990845.

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Tichomirova, T. M., and A. G. Sukiasyan. Test bank «Time Series Models». Ailamazyan Program Systems Institute of Russian Academy of Sciences, November 2023. http://dx.doi.org/10.12731/ofernio.2023.25215.

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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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Glynn, P. W., and D. L. Iglehart. The Theory of Standardized Time Series. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada158383.

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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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Jääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, September 2023. http://dx.doi.org/10.35614/isbn.9789523361782.

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A reliable satellite-based black-sky albedo time series is a crucial part of detecting changes in the climate. This thesis studies the solutions to several uncertainties impairing the quality of the black-sky albedo time series. These solutions include creating a long dynamic aerosol optical depth time series for enhancing the removal of atmospheric effects, a method to fill missing data to improve spatial and temporal coverage, and creating a function to correctly model the diurnal variation of melting snow albedo. Mathematical methods are the center pieces of the solutions found in this thesis. Creating a melting snow albedo function and the construction of an aerosol optical depth time series lean on a linear regression approach, whereas the process to fill missing values is based on gradient boosting, a machine learning method that is in turn based on decision trees. These methods reflect the basic nature of these problems as well as the need to take into account the large amounts of satellite-based data and computational resources available.
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DeSmet, Chance, Michael Girard, Elizabeth Coda, and Yijing Watkins. Universal Fourier Attack for Time Series. Office of Scientific and Technical Information (OSTI), September 2023. http://dx.doi.org/10.2172/2338170.

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