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Готові списки джерел за темами / Models of time

Добірка наукової літератури з теми "Models of time"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Models of time"

1

Ziegel, Eric R., D. R. Cox, D. V. Hinkley, and O. E. Barndorff-nielsen. "Time Series Models." Technometrics 39, no. 1 (February 1997): 110. http://dx.doi.org/10.2307/1270795.

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2

Ljung, Greta M., and Andrew C. Harvey. "Time Series Models." Journal of the American Statistical Association 90, no. 429 (March 1995): 394. http://dx.doi.org/10.2307/2291179.

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3

Ensor, Katherine B. "Time Series Models." Technometrics 37, no. 4 (November 1995): 464–65. http://dx.doi.org/10.1080/00401706.1995.10484390.

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4

Declerck, Philippe. "Extremum Cycle Times in Time Interval Models." IEEE Transactions on Automatic Control 63, no. 6 (June 2018): 1821–27. http://dx.doi.org/10.1109/tac.2017.2757085.

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5

Sokannit, Patcharakorn. "Forecasting Household Electricity Consumption Using Time Series Models." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 380–86. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1065.

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6

Gadda, Shashank, Kara M. Kockelman, and Paul Damien. "Continuous Departure Time Models." Transportation Research Record: Journal of the Transportation Research Board 2132, no. 1 (January 2009): 13–24. http://dx.doi.org/10.3141/2132-02.

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7

Crane, Harry. "Time-varying network models." Bernoulli 21, no. 3 (August 2015): 1670–96. http://dx.doi.org/10.3150/14-bej617.

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8

Cochrane, John. "Continuous-Time Linear Models." Foundations and Trends® in Finance 6, no. 3 (2011): 165–219. http://dx.doi.org/10.1561/0500000037.

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9

Santos, Douglas Matheus das Neves, Yuri Antônio da Silva Rocha, Danúbia Freitas, Paulo Beltrão, Paulo Santos Junior, Glauber Marques, Otavio Chase, and Pedro Campos. "Time-series forecasting models." International Journal for Innovation Education and Research 9, no. 8 (August 1, 2021): 24–47. http://dx.doi.org/10.31686/ijier.vol9.iss8.3239.

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Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.

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10

Chatfield, Chris. "Periodic Time Series Models." Journal of the Royal Statistical Society: Series A (Statistics in Society) 168, no. 3 (July 2005): 632–33. http://dx.doi.org/10.1111/j.1467-985x.2005.00368_6.x.

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Дисертації з теми "Models of time"

1

Billah, Baki 1965. "Model selection for time series forecasting models." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8840.

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2

Ambler, Gareth. "Time varying-coefficient models." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321345.

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3

Jähnichen, Patrick. "Time Dynamic Topic Models." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-200796.

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Information extraction from large corpora can be a useful tool for many applications in industry and academia. For instance, political communication science has just recently begun to use the opportunities that come with the availability of massive amounts of information available through the Internet and the computational tools that natural language processing can provide. We give a linguistically motivated interpretation of topic modeling, a state-of-the-art algorithm for extracting latent semantic sets of words from large text corpora, and extend this interpretation to cover issues and issue-cycles as theoretical constructs coming from political communication science. We build on a dynamic topic model, a model whose semantic sets of words are allowed to evolve over time governed by a Brownian motion stochastic process and apply a new form of analysis to its result. Generally this analysis is based on the notion of volatility as in the rate of change of stocks or derivatives known from econometrics. We claim that the rate of change of sets of semantically related words can be interpreted as issue-cycles, the word sets as describing the underlying issue. Generalizing over the existing work, we introduce dynamic topic models that are driven by general (Brownian motion is a special case of our model) Gaussian processes, a family of stochastic processes defined by the function that determines their covariance structure. We use the above assumption and apply a certain class of covariance functions to allow for an appropriate rate of change in word sets while preserving the semantic relatedness among words. Applying our findings to a large newspaper data set, the New York Times Annotated corpus (all articles between 1987 and 2007), we are able to identify sub-topics in time, \\\\textit{time-localized topics} and find patterns in their behavior over time. However, we have to drop the assumption of semantic relatedness over all available time for any one topic. Time-localized topics are consistent in themselves but do not necessarily share semantic meaning between each other. They can, however, be interpreted to capture the notion of issues and their behavior that of issue-cycles.

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4

Petersson, Mikael. "Perturbed discrete time stochastic models." Doctoral thesis, Stockholms universitet, Matematiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-128979.

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In this thesis, nonlinearly perturbed stochastic models in discrete time are considered. We give algorithms for construction of asymptotic expansions with respect to the perturbation parameter for various quantities of interest. In particular, asymptotic expansions are given for solutions of renewal equations, quasi-stationary distributions for semi-Markov processes, and ruin probabilities for risk processes.

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Manuscript. Paper 5: Manuscript. Paper 6: Manuscript.

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5

Harrison, Martin. "Time in quality constrained models." Thesis, University of Southampton, 1987. https://eprints.soton.ac.uk/361656/.

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6

Ehlers, Ricardo Sandes. "Bayesian model discrimination for time series and state space models." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843599/.

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In this thesis, a Bayesian approach is adopted to handle parameter estimation and model uncertainty in autoregressive moving average (ARMA) time series models and dynamic linear models (DLM). Bayesian model uncertainty is handled in a parametric fashion through the use of posterior model probabilities computed via Markov chain Monte Carlo (MCMC) simulation techniques. Attention is focused on reversible jump Markov chain Monte Carlo (RJMCMC) samplers, which can move between models of different dimensions, to address the problem of model order uncertainty and strategies for proposing efficient sampling schemes in autoregressive moving average time series models and dynamic linear models are developed. The general problem of assessing convergence of the sampler in a dimension-changing context is addressed by computing estimates of the probabilities of moving to higher and lower dimensional spaces. Graphical and numerical techniques are used to compare different updating schemes. The methodology is illustrated by applying it to both simulated and real data sets and the results for the Bayesian model selection and parameter estimation procedures are compared with the classical model selection criteria and maximum likelihood estimation.

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7

Mroz, Magda [Verfasser]. "Time-varying copula models for financial time series / Magda Mroz." Ulm : Universität Ulm. Fakultät für Mathematik und Wirtschaftswissenschaften, 2012. http://d-nb.info/1027341578/34.

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8

Price, David Charles. "History matching hydromechanical models using time-lapse seismic time-shifts." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/21733/.

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Although time-lapse seismic data has been used to great success in the history matching of reservoir fluid properties (i.e. saturation in reservoir simulators), it has been used far less effectively for benchmarking geomechanical behaviour. The reason for this is twofold. Firstly, hydromechanical models are typically large, complex and highly nonlinear with considerably large runtimes. Secondly, isolating and extracting quantifiable mechanical information from seismic data is difficult. However, by not attempting to utilise numerical history matching techniques, are we making the most out of the geomechanical information stored in time-lapse seismic data? In this Thesis I have attempted to answer this question by conducting a synthetic history matching study. I generate a hydromechanical model of a typical high pressure high temperature production scenario in the North Sea and utilise seismic history matching in an attempt to constrain the properties of the overburden and improve the models predictive capabilities. The study focuses primarily on overburden calibration as overburden timeshifts are not complicated by fluid effects, as in the reservoir, and hence can be considered as a purely geomechanical effect. Also the matching process is attempted utilising only a small, feasible number of model perturbations. Before seismic history matching can be successfully attempted it is important to have an in depth working knowledge of the model behaviour. Therefore, I conduct a multi-method Global Sensitivity Analysis (GSA) on over 4000 model perturbations, to evaluate the potential geomechanical information content of seismic time-shifts. Specifically, which model parameters cause the majority of the variation to overburden time-shifts. The results show that the majority of the variation in modelled shifts can be attributed to the Young's Modulus and Biot coefficient. These parameters appear the most influential for both near-offset time-shifts and the time-shift offset behaviour. However, the Poisson's ratio also becomes influential when considering the time-shift offset behaviour at long offsets. The results of the GSA also highlight that the over-parametrisation of material properties in the model can lead to unnecessary complexity in the model space. The simplification of complex rock properties (i.e. simplification of nonlinear relationships to single constants) will not significantly affect model performance whilst making seismic history matching more achievable. A robust history matching study also requires the consideration of all forms of uncertainty. One of the main causes of uncertainty in the process is that of the relationship between effective stress and seismic velocity i.e. the rock physics model. I analyse a handful of the most popular rock physics models and assess their behaviour and stability when applied to a large dry core dataset of different lithologies. The results show that most models are robust, well constrained and do a suitably good job at fitting velocity-stress data taken from core samples in a laboratory environment. However, slight discrepancies between different model approximations for the same core sample can cause significantly different time-lapse velocity predictions. The results also show that models are difficult to parameterise without the availability of velocity-stress core data. Attempting to do so can lead to even greater discrepancies in their time-lapse velocity predictions. The results also support the current belief that the velocity-stress core data may not be a good representation of the velocity-stress dependence of the subsurface I utilise an iterative emulator based approach to history matching which makes it possible to perform a robust history match with a small number of model realisations. I utilise the results of the GSA to define the model parameters in which to focus the history match and also utilise the results of the rock physics model analysis to define suitable uncertainties. The results of the emulation process show it is possible to perform a successful history match utilising only a small number of model perturbations and to constrain the uncertainty in the most influential model parameters. The process is improved significantly when both near-offset time-shifts and the time-shift offset behaviour are considered simultaneously in the matching process. It becomes apparent that the matching process and hence final solution is limited by the number of model realisations, iterations and the extent of the available seismic data. The greater the number of realisations, the more accurate the emulators whilst the more seismic observations, the more data available in which to test predicted models. Also, it becomes increasingly clear that the uncertainty in rock physics modelling dominates the matching process. Taking into consideration it's uncertainty makes it extremely difficult to confidently constrain any properties of the hydromechanical model from time-lapse seismic data. It becomes increasingly apparent that there is a great need to improve our understanding of rock behaviour (i.e. rock physics) before the seismic history matching of mechanical behavior becomes suitably accurate and economically appealing.

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9

Wedi, Nils Peter. "Time-dependent boundaries in numerical models." Diss., lmu, 2005. http://nbn-resolving.de/urn:nbn:de:bvb:19-31420.

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10

Sjolander, Morne Rowan. "Time series models for paired comparisons." Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/d1012858.

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The method of paired comparisons is seen as a technique used to rank a set of objects with respect to an abstract or immeasurable property. To do this, the objects get to be compared two at a time. The results are input into a model, resulting in numbers known as weights being assigned to the objects. The weights are then used to rank the objects. The method of paired comparisons was first used for psychometric investigations. Various other applications of the method are also present, for example economic applications, and applications in sports statistics. This study involves taking paired comparison models and making them time-dependent. Not much research has been done in this area. Three new time series models for paired comparisons are created. Simulations are done to support the evidence obtained, and theoretical as well as practical examples are given to illustrate the results and to verify the efficiency of the new models. A literature study is given on the method of paired comparisons, as well as on the areas in which we apply our models. Our first two time series models for paired comparisons are the Linear-Trend Bradley- Terry Model and the Sinusoidal Bradley-Terry Model. We use the maximum likelihood approach to solve these models. We test our models using exact and randomly simulated data for various time periods and various numbers of objects. We adapt the Linear-Trend Bradley-Terry Model and received our third time series model for paired comparisons, the Log Linear-Trend Bradley-Terry Model. The daily maximum and minimum temperatures were received for Port Elizabeth, Uitenhage and Coega for 2005 until 2009. To evaluate the performance of the Linear-Trend Bradley-Terry Model and the Sinusoidal Bradley-Terry Model on estimating missing temperature data, we artificially remove observations of temperature from Coega’s temperature dataset for 2006 until 2008, and use various forms of these models to estimate the missing data points. The exchange rates for 2005 until 2008 between the following currencies: the Rand, Dollar, Euro, Pound and Yen, were obtained and various forms of our Log Linear-Trend Bradley-Terry Model are used to forecast the exchange rate for one day ahead for each month in 2006 until 2008. One of the features of this study is that we apply our time series models for paired comparisons to areas which comprise non-standard paired comparisons; and we want to encourage the use of the method of paired comparisons in a broader sense than what it is traditionally used for. The results of this study can be used in various other areas, like for example, in sports statistics, to rank the strength of sports players and predict their future scores; in Physics, to calculate weather risks of electricity generation, particularly risks related to nuclear power plants, and so forth, as well as in many other areas. It is hoped that this research will open the door to much more research in combining time series analysis with the method of paired comparisons.

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Книги з теми "Models of time"

1

Harvey, A. C. Time series models. 2nd ed. New York: Harvester Wheatsheaf, 1993.

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2

Harvey, A. C. Time series models. 2nd ed. New York: Harvester Wheatsheaf, 1992.

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3

Time series models. 2nd ed. Cambridge, Mass: MIT Press, 1993.

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4

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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5

Cox, D. R., D. V. Hinkley, and O. E. Barndorff-Nielsen, eds. Time Series Models. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-2879-5.

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6

Brandt, Patrick, and John Williams. Multiple Time Series Models. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2007. http://dx.doi.org/10.4135/9781412985215.

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7

Franses, Philip Hans. Periodic time series models. Oxford: Oxford University Press, 2004.

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8

Barber, David, A. Taylan Cemgil, and Silvia Chiappa, eds. Bayesian Time Series Models. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511984679.

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9

1958-, Williams John T., ed. Multiple time series models. Thousand Oaks, Calif: Sage Publications, 2007.

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10

Pearson, Ronald K. Discrete-time dynamic models. New York: Oxford University Press, 1999.

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Частини книг з теми "Models of time"

1

Prado, Raquel, Marco A. R. Ferreira, and Mike West. "Dynamic linear models." In Time Series, 131–68. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351259422-4.

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2

Prado, Raquel, Marco A. R. Ferreira, and Mike West. "Latent factor models." In Time Series, 359–408. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351259422-11.

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3

Söderström, T. "Models." In Discrete-time Stochastic Systems, 29–58. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0101-7_3.

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4

Prado, Raquel, Marco A. R. Ferreira, and Mike West. "State-space TVAR models." In Time Series, 169–88. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351259422-5.

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5

Prado, Raquel, Marco A. R. Ferreira, and Mike West. "Traditional time domain models." In Time Series, 35–96. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351259422-2.

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6

Accardi, Luigi. "Models of Time." In Direction of Time, 199–232. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02798-2_18.

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7

Deistler, Manfred, and Wolfgang Scherrer. "Dynamic Factor Models." In Time Series Models, 175–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1_10.

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8

Harvey, A. C. "ARIMA Models." In Time Series and Statistics, 22–24. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_2.

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9

Sengupta, Jati K., and Phillip Fanchon. "Continuous time models." In Control Theory Methods in Economics, 9–62. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6285-6_2.

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10

Sengupta, Jati K., and Phillip Fanchon. "Discrete time models." In Control Theory Methods in Economics, 63–96. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6285-6_3.

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Тези доповідей конференцій з теми "Models of time"

1

Famelis, Michalis, and Marsha Chechik. "Managing Design-Time Uncertainty." In 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.24.

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2

Benelallam, Amine, Thomas Hartmann, Ludovic Mouline, Francois Fouquet, Johann Bourcier, Olivier Barais, and Yves Le Traon. "Raising Time Awareness in Model-Driven Engineering: Vision Paper." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.11.

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3

Wilson, R. "Image analysis and segmentation using mixture models." In IEE Seminar on Time-Scale and Time-Frequency Analysis and Applications. IEE, 2000. http://dx.doi.org/10.1049/ic:20000560.

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4

Bencomo, Nelly. "Modeling Autonomic Systems in the time of ML, DevOps and Microservices." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00118.

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5

Seviora, R. E. "Models for Real-Time Supervision." In Fifth Euromicro Workshop on Real-Time Systems. IEEE, 1993. http://dx.doi.org/10.1109/emwrt.1993.639100.

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6

Ahmadi, Reza, Ernesto Posse, and Juergen Dingel. "Slicing UML-based Models of Real-time Embedded Systems." In MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3239372.3239407.

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7

Jahanbin, Sorour, Dimitris Kolovos, and Simos Gerasimou. "Intelligent run-time partitioning of low-code system models." In MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3417990.3420198.

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8

Gotzhein, Reinhard. "On the Conception of Executable Design Languages for Distributed Real-time Systems." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00119.

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9

Gruber, Sebastian, Bernd Neumayr, Michael Schrefl, and Josef Niebauer. "Towards Multi-level Modeling of Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00084.

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10

Tiger, Mattias, and Fredrik Heintz. "Stream Reasoning Using Temporal Logic and Predictive Probabilistic State Models." In 2016 23rd International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2016. http://dx.doi.org/10.1109/time.2016.28.

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Звіти організацій з теми "Models of time"

1

Campos, Sergio V., and Edmund M. Clarke. Real-Time Symbolic Model Checking for Discrete Time Models. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada282878.

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2

Cochrane, John. Continuous-Time Linear Models. Cambridge, MA: National Bureau of Economic Research, June 2012. http://dx.doi.org/10.3386/w18181.

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3

Backus, David, Silverio Foresi, and Chris Telmer. Discrete-Time Models of Bond Pricing. Cambridge, MA: National Bureau of Economic Research, September 1998. http://dx.doi.org/10.3386/w6736.

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4

Cai, Yongyang, Kenneth Judd, and Thomas Lontzek. Continuous-Time Methods for Integrated Assessment Models. Cambridge, MA: National Bureau of Economic Research, September 2012. http://dx.doi.org/10.3386/w18365.

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5

Glickman, Mark E. Paired Comparison Models with Time-Varying Parameters. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada272016.

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6

Alvarez, Fernando, Francesco Lippi, and Juan Passadore. Are State and Time Dependent Models Really Different? Cambridge, MA: National Bureau of Economic Research, June 2016. http://dx.doi.org/10.3386/w22361.

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7

Zimmerman, Nicole. Time-Variant Load Models of Electric Vehicle Chargers. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2294.

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8

Chen, Xiaohong, ., and Yixiao Sun. Sieve inference on semi-nonparametric time series models. Institute for Fiscal Studies, February 2012. http://dx.doi.org/10.1920/wp.cem.2012.0612.

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9

Kedem, B. A Graphical Similarity Measure for Time Series Models. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada158869.

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10

Bassiouni, M., Michael Georgiopoulos, and Jack Thompson. Analytical and Simulation Models for Real-Time Networks. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada241021.

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