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Artykuły w czasopismach na temat "Time series aggregation"
Rossana, Robert J., i John J. Seater. "Temporal Aggregation and Economic Time Series". Journal of Business & Economic Statistics 13, nr 4 (październik 1995): 441. http://dx.doi.org/10.2307/1392389.
Pełny tekst źródłaRossana, Robert J., i John J. Seater. "Temporal Aggregation and Economic Time Series". Journal of Business & Economic Statistics 13, nr 4 (październik 1995): 441–51. http://dx.doi.org/10.1080/07350015.1995.10524618.
Pełny tekst źródłaBrännäs, Kurt, i Henry Ohlsson. "Asymmetric Time Series and Temporal Aggregation". Review of Economics and Statistics 81, nr 2 (maj 1999): 341–44. http://dx.doi.org/10.1162/003465399558120.
Pełny tekst źródłaYager, Ronald R. "Time Series Smoothing and OWA Aggregation". IEEE Transactions on Fuzzy Systems 16, nr 4 (sierpień 2008): 994–1007. http://dx.doi.org/10.1109/tfuzz.2008.917299.
Pełny tekst źródłade Jong, R., i S. de Bruin. "Time series of vegetation indices and the modifiable temporal unit problem". Biogeosciences Discussions 8, nr 4 (24.08.2011): 8545–61. http://dx.doi.org/10.5194/bgd-8-8545-2011.
Pełny tekst źródłaCelov, Dmitrij, i Remigijus Leipus. "Time series aggregation, disaggregation and long memory". Lietuvos matematikos rinkinys 46 (21.09.2023): 255–62. http://dx.doi.org/10.15388/lmr.2006.30723.
Pełny tekst źródłaZhu, Ye, Yongjian Fu i Huirong Fu. "Preserving Privacy in Time Series Data Mining". International Journal of Data Warehousing and Mining 7, nr 4 (październik 2011): 64–85. http://dx.doi.org/10.4018/jdwm.2011100104.
Pełny tekst źródłaBeran, Jan, Haiyan Liu i Sucharita Ghosh. "On aggregation of strongly dependent time series". Scandinavian Journal of Statistics 47, nr 3 (13.12.2019): 690–710. http://dx.doi.org/10.1111/sjos.12421.
Pełny tekst źródłaKim, Hung Soo, Yong Nam Yoon, Gyu-Sei Yi i Taegyun Kim. "Effect of aggregation on chaotic time series". KSCE Journal of Civil Engineering 4, nr 4 (grudzień 2000): 219–26. http://dx.doi.org/10.1007/bf02823969.
Pełny tekst źródłaChipman, J., i P. Winker. "Optimal aggregation of linear time series models". Computational Statistics & Data Analysis 49, nr 2 (kwiecień 2005): 311–31. http://dx.doi.org/10.1016/j.csda.2004.05.015.
Pełny tekst źródłaRozprawy doktorskie na temat "Time series aggregation"
Tripodis, Georgios. "Heterogeneity and aggregation in seasonal time series". Thesis, London School of Economics and Political Science (University of London), 2007. http://etheses.lse.ac.uk/2933/.
Pełny tekst źródłaLin, Shu-Chin. "Aggregation and time series implications of state-dependent consumption /". free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737881.
Pełny tekst źródłaSariaslan, Nazli. "The Effect Of Temporal Aggregation On Univariate Time Series Analysis". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612528/index.pdf.
Pełny tekst źródłaGehman, Andrew J. "The Effects of Spatial Aggregation on Spatial Time Series Modeling and Forecasting". Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/382669.
Pełny tekst źródłaPh.D.
Spatio-temporal data analysis involves modeling a variable observed at different locations over time. A key component of space-time modeling is determining the spatial scale of the data. This dissertation addresses the following three questions: 1) How does spatial aggregation impact the properties of the variable and its model? 2) What spatial scale of the data produces more accurate forecasts of the aggregate variable? 3) What properties lead to the smallest information loss due to spatial aggregation? Answers to these questions involve a thorough examination of two common space-time models: the STARMA and GSTARMA models. These results are helpful to researchers seeking to understand the impact of spatial aggregation on temporal and spatial correlation as well as to modelers interested in determining a spatial scale for the data. Two data examples are included to illustrate the findings, and they concern states' annual labor force totals and monthly burglary counts for police districts in the city of Philadelphia.
Temple University--Theses
Kim, Hang. "TIME SERIES BLOCK BOOTSTRAP APPLICATION AND EFFECT OF AGGREGATION AND SYSTEMATIC SAMPLING". Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/490644.
Pełny tekst źródłaPh.D.
In this dissertation, we review the basic properties of the bootstrap and time series application. Then we apply parametric bootstrap on three simulated normal i.i.d. samples and nonparametric bootstrap on four real life financial returns. Among the time series bootstrap methods, we look into the specific method called block bootstrap and investigate the block length consideration to properly select a suitable block size for AR(1) model. We propose a new rule of blocking named as Combinatorially-Augmented Block Bootstrap(CABB). We compare the existing block bootstrap and CABB method using the simulated i.i.d. samples, AR(1) time series, and the real life examples. Both methods perform equally well in estimating AR(1) coefficients. CABB produces a smaller standard deviation based on our simulated and empirical studies. We study two procedures of collecting time series, (i) aggregation of a flow variable and (ii) systematic sampling of a stock variable. In these two procedures, we derive theorems that calculate exact equations for $m$ aggregated and $m^{th}$ systematically sampled series of the original AR(1) model. We evaluate the performance of block bootstrap estimation of the parameters of ARMA(1,1) and AR(1) model using aggregated and systematically sampled series. Simulation and real data analyses show that in some cases, the performance of the estimation based on the block bootstrap method for the MA(1) parameter of the ARMA(1,1) model in aggregated series is better than the one without using bootstrap. In an extreme case of stock price movement, which is close to a random walk, the block bootstrap estimate using systematically sampled series is closer to the true parameter, defined as the parameter calculated by the theorem. Specifically, the block bootstrap estimate of the parameter of AR(1) model using the systematically sampled series is closer to phi(n) than that based on the MLE for the AR(1) model. Future research problems include theoretical investigation of CABB, effectiveness of block bootstrap in other time series analyses such as nonlinear or VAR.
Temple University--Theses
APRAEZ, CESAR DAVID REVELO. "A HYBRID NEURO- EVOLUTIONARY APPROACH FOR DYNAMIC WEIGHTED AGGREGATION OF TIME SERIES FORECASTERS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36950@1.
Pełny tekst źródłaCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Estudos empíricos na área de séries temporais indicam que combinar modelos preditivos, originados a partir de diferentes técnicas de modelagem, levam a previsões consensuais superiores, em termos de acurácia, às previsões individuais dos modelos envolvidos na combinação. No presente trabalho é apresentada uma metodologia de combinação convexa de modelos estatísticos de previsão, cujo sucesso depende da forma como os pesos de combinação de cada modelo são estimados. Uma Rede Neural Artificial Perceptron Multi-camada (Multilayer Perceptron - MLP) é utilizada para gerar dinamicamente vetores de pesos ao longo do horizonte de previsão, sendo estes dependentes da contribuição individual de cada previsor observada nos dados históricos da série. O ajuste dos parâmetros da rede MLP é efetuado através de um algoritmo de treinamento híbrido, que integra técnicas de busca global, baseadas em computação evolucionária, junto com o algoritmo de busca local backpropagation, de modo a otimizar de forma simultânea tanto os pesos quanto a arquitetura da rede, visando, assim, a gerar de forma automática um modelo de ponderação dinâmica de previsores de alto desempenho. O modelo proposto, batizado de Neural Expert Weighting - Genetic Algorithm (NEW-GA), foi avaliado em diversos experimentos comparativos com outros modelos de ponderação de previsores, assim como também com os modelos individuais envolvidos na combinação, contemplando 15 séries temporais divididas em dois estudos de casos: séries de derivados de petróleo e séries da versão reduzida da competição NN3, uma competição entre metodologias de previsão, com maior ênfase nos modelos baseados em Redes Neurais. Os resultados demonstraram o potencial do NEWGA em fornecer modelos acurados de previsão de séries temporais.
Empirical studies on time series indicate that the combination of forecasting models, generated from different modeling techniques, leads to higher consen+sus forecasts, in terms of accuracy, than the forecasts of individual models involved in the combination scheme. In this work, we present a methodology for convex combination of statistical forecasting models, whose success depends on how the combination weights of each model are estimated. An Artificial Neural Network Multilayer Perceptron (MLP) is used to generate dynamically weighting vectors over the forecast horizon, being dependent on the individual contribution of each forecaster observed over historical data series. The MLP network parameters are adjusted via a hybrid training algorithm that integrates global search techniques, based on evolutionary computation, along with the local search algorithm backpropagation, in order to optimize simultaneously both weights and network architecture. This approach aims to automatically generate a dynamic weighted forecast aggregation model with high performance. The proposed model, called Neural Expert Weighting - Genetic Algorithm (NEW-GA), was com- pared with other forecaster combination models, as well as with the individual models involved in the combination scheme, comprising 15 time series divided into two case studies: Petroleum Products and the reduced set of NN3 forecasting competition, a competition between forecasting methodologies, with greater emphasis on models based on neural networks. The results obtained demonstrated the potential of NEW-GA in providing accurate models for time series forecasting.
Weiss, Christoph. "Essays in hierarchical time series forecasting and forecast combination". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274757.
Pełny tekst źródłaDoell, Christoph [Verfasser]. "Methods for Multivariate Time-Series Classification on Brain Data : Aggregation, Stratification and Neural Network Models / Christoph Doell". Konstanz : KOPS Universität Konstanz, 2021. http://d-nb.info/1232176648/34.
Pełny tekst źródłaBahl, Björn [Verfasser], André [Akademischer Betreuer] Bardow i Francois [Akademischer Betreuer] Marechal. "Optimization-based synthesis of large-scale energy systems by time-series aggregation / Björn Bahl ; André Bardow, Francois Marechal". Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1186069260/34.
Pełny tekst źródłaLee, Bu Hyoung. "The use of temporally aggregated data on detecting a structural change of a time series process". Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/375511.
Pełny tekst źródłaPh.D.
A time series process can be influenced by an interruptive event which starts at a certain time point and so a structural break in either mean or variance may occur before and after the event time. However, the traditional statistical tests of two independent samples, such as the t-test for a mean difference and the F-test for a variance difference, cannot be directly used for detecting the structural breaks because it is almost certainly impossible that two random samples exist in a time series. As alternative methods, the likelihood ratio (LR) test for a mean change and the cumulative sum (CUSUM) of squares test for a variance change have been widely employed in literature. Another point of interest is temporal aggregation in a time series. Most published time series data are temporally aggregated from the original observations of a small time unit to the cumulative records of a large time unit. However, it is known that temporal aggregation has substantial effects on process properties because it transforms a high frequency nonaggregate process into a low frequency aggregate process. In this research, we investigate the effects of temporal aggregation on the LR test and the CUSUM test, through the ARIMA model transformation. First, we derive the proper transformation of ARIMA model orders and parameters when a time series is temporally aggregated. For the LR test for a mean change, its test statistic is associated with model parameters and errors. The parameters and errors in the statistic should be changed when an AR(p) process transforms upon the mth order temporal aggregation to an ARMA(P,Q) process. Using the property, we propose a modified LR test when a time series is aggregated. Through Monte Carlo simulations and empirical examples, we show that the aggregation leads the null distribution of the modified LR test statistic being shifted to the left. Hence, the test power increases as the order of aggregation increases. For the CUSUM test for a variance change, we show that two aggregation terms will appear in the test statistic and have negative effects on test results when an ARIMA(p,d,q) process transforms upon the mth order temporal aggregation to an ARIMA(P,d,Q) process. Then, we propose a modified CUSUM test to control the terms which are interpreted as the aggregation effects. Through Monte Carlo simulations and empirical examples, the modified CUSUM test shows better performance and higher test powers to detect a variance change in an aggregated time series than the original CUSUM test.
Temple University--Theses
Książki na temat "Time series aggregation"
Zaffaroni, Paolo. Contemporaneous aggregation of GARCH processes. Roma: Banca d'Italia, 2002.
Znajdź pełny tekst źródłaChambers, Marcus J. Long memory and aggregation in macroeconomic time series. Colchester: Essex University, Department of Economics, 1995.
Znajdź pełny tekst źródłaLippi, Marco. Aggregation of simple linear dynamics: Exact asymptotic results. London: Suntory Centre, 1998.
Znajdź pełny tekst źródłaUrga, Giovanni. Panel data vs. time series regression analysis: An aggregation issue : a comparison using labour demand functions. London: London University, Queen Mary and Westfield College, Department of Economics, 1993.
Znajdź pełny tekst źródłaForecasting Aggregated Vector ARMA Processes. Berlin: Springer-Verlag, 1987.
Znajdź pełny tekst źródłaPevehouse, Jon, i Jason D. Brozek. Time‐Series Analysis. Redaktorzy Janet M. Box-Steffensmeier, Henry E. Brady i David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0019.
Pełny tekst źródłaFazekas, Mihály, Luciana Cingolani i Bence Tóth. Innovations in Objectively Measuring Corruption in Public Procurement. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198817062.003.0007.
Pełny tekst źródłaCzęści książek na temat "Time series aggregation"
Stoker, Thomas M. "Aggregation (econometrics)". W Macroeconometrics and Time Series Analysis, 1–14. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_1.
Pełny tekst źródłaDrost, Feike C. "Temporal Aggregation of Time-Series". W Econometric Analysis of Financial Markets, 11–21. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-48666-1_2.
Pełny tekst źródłaYager, Ronald R. "Intelligent Aggregation and Time Series Smoothing". W Time Series Analysis, Modeling and Applications, 53–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33439-9_3.
Pełny tekst źródłaHouthakker, H. S. "Proposed Technique for Estimating Demand Functions from Time Series". W Aggregation, Consumption and Trade, 255–56. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-1795-1_16.
Pełny tekst źródłaAkbarinia, Reza, i Florent Masseglia. "Aggregation-Aware Compression of Probabilistic Streaming Time Series". W Machine Learning and Data Mining in Pattern Recognition, 232–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21024-7_16.
Pełny tekst źródłaValovich, Filipp. "Aggregation of Time-Series Data Under Differential Privacy". W Progress in Cryptology – LATINCRYPT 2017, 249–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25283-0_14.
Pełny tekst źródłaCastillo, Oscar, i Patricia Melin. "Type-3 Fuzzy Aggregation of Neural Networks". W Type-3 Fuzzy Logic in Time Series Prediction, 49–59. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59714-5_5.
Pełny tekst źródłaDurante, Fabrizio. "Copulas, Tail Dependence and Applications to the Analysis of Financial Time Series". W Aggregation Functions in Theory and in Practise, 17–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39165-1_3.
Pełny tekst źródłaLeontiadis, Iraklis, Kaoutar Elkhiyaoui i Refik Molva. "Private and Dynamic Time-Series Data Aggregation with Trust Relaxation". W Cryptology and Network Security, 305–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12280-9_20.
Pełny tekst źródłaSchurtenberger, Peter, Hugo Bissig, Luis Rojas, Ronny Vavrin, Anna Stradner, Sara Romer, Frank Scheffold i Veronique Trappe. "Aggregation and Gelation in Colloidal Suspensions: Time-Resolved Light and Neutron Scattering Experiments". W ACS Symposium Series, 143–60. Washington, DC: American Chemical Society, 2003. http://dx.doi.org/10.1021/bk-2003-0861.ch009.
Pełny tekst źródłaStreszczenia konferencji na temat "Time series aggregation"
Iosevich, S., G. Arutyunyants i Z. Hou. "Dynamic aggregation for time series forecasting". W 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363996.
Pełny tekst źródłade Oliveira, Ricardo T. A., Thaize Fernandes O. de Assis, Paulo Renato A. Firmino, Tiago A. E. Ferreira i Adriano L. I. Oliveira. "Aggregation of Time Series Forecasts via Cacoullos Copula". W 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489098.
Pełny tekst źródłaZhou, Ming, i Ye Chen. "An aggregation framework for time series-based MCDA". W 2011 International Conference on Grey Systems and Intelligent Services (GSIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/gsis.2011.6044019.
Pełny tekst źródłaBakondi, Bence Gabor, Andreas Peter, Maarten Everts, Pieter Hartel i Willem Jonker. "Publicly Verifiable Private Aggregation of Time-Series Data". W 2015 10th International Conference on Availability, Reliability and Security (ARES). IEEE, 2015. http://dx.doi.org/10.1109/ares.2015.82.
Pełny tekst źródłaGujulla Leel, Srini Rohan, Vikrant Dey, Puspita Majumdar i Ankit Khairkar. "HierTGAN: Hierarchical Time Series Generation with Aggregation Constraints". W CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD). New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3632410.3632444.
Pełny tekst źródłaYang, Peilin, Srikanth Thiagarajan i Jimmy Lin. "Robust, Scalable, Real-Time Event Time Series Aggregation at Twitter". W SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3190663.
Pełny tekst źródłaWerner, Gordon, Ahmet Okutan, Shanchieh Yang i Katie McConky. "Forecasting Cyberattacks as Time Series with Different Aggregation Granularity". W 2018 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 2018. http://dx.doi.org/10.1109/ths.2018.8574185.
Pełny tekst źródłaAlbers, Danielle, Michael Correll i Michael Gleicher. "Task-driven evaluation of aggregation in time series visualization". W CHI '14: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2556288.2557200.
Pełny tekst źródłaSaadallah, Amal, Maryam Tavakol i Katharina Morik. "An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting". W 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00233.
Pełny tekst źródłaKramakum, Chutimol, Thanawin Rakthanmanon i Kitsana Waiyamai. "Information gain Aggregation-based Approach for Time Series Shapelets Discovery". W 2018 10th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2018. http://dx.doi.org/10.1109/kse.2018.8573365.
Pełny tekst źródłaRaporty organizacyjne na temat "Time series aggregation"
Jensen, Hans Grinsted, i Kym Anderson. Alternative Agricultural Price Distortions for CGE Analysis, 2007 and 2011. GTAP Research Memoranda, kwiecień 2014. http://dx.doi.org/10.21642/gtap.rm27.
Pełny tekst źródłaHertel, Thomas, David Hummels, Maros Ivanic i Roman Keeney. How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? GTAP Working Paper, czerwiec 2003. http://dx.doi.org/10.21642/gtap.wp26.
Pełny tekst źródłaMartínez-Rivera, Wilmer, Eliana R. González-Molano i Edgar Caicedo-García. Forecasting Inflation from Disaggregated Data: The Colombian case. Banco de la República, październik 2023. http://dx.doi.org/10.32468/be.1251.
Pełny tekst źródłaTsur, Yacov, David Zilberman, Uri Shani, Amos Zemel i David Sunding. Dynamic intraseasonal irrigation management under water scarcity, water quality, irrigation technology and environmental constraints. United States Department of Agriculture, marzec 2007. http://dx.doi.org/10.32747/2007.7696507.bard.
Pełny tekst źródłaChefetz, Benny, Baoshan Xing, Leor Eshed-Williams, Tamara Polubesova i Jason Unrine. DOM affected behavior of manufactured nanoparticles in soil-plant system. United States Department of Agriculture, styczeń 2016. http://dx.doi.org/10.32747/2016.7604286.bard.
Pełny tekst źródłaEparkhina, Dina. EuroSea Legacy Report. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d8.12.
Pełny tekst źródłaOpportunities, challenges and evidence needs for investing in smallholder farming. Commercial Agriculture for Smallholders and Agribusiness (CASA), 2019. http://dx.doi.org/10.1079/20240191176.
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