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Journal articles on the topic 'Models of time'

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Published: 10 December 2022
Last updated: 28 January 2023

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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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11

Chan, Joshua C. C., Gary Koop, Roberto Leon-Gonzalez, and Rodney W. Strachan. "Time Varying Dimension Models." Journal of Business & Economic Statistics 30, no. 3 (July 2012): 358–67. http://dx.doi.org/10.1080/07350015.2012.663258.

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12

Burnor, Richard N. "Modal Models of Time." Southern Journal of Philosophy 38, no. 1 (March 2000): 19–37. http://dx.doi.org/10.1111/j.2041-6962.2000.tb00889.x.

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13

Fotopoulos, Stergios B. "Discrete-Time Dynamic Models." Technometrics 43, no. 1 (February 2001): 110–11. http://dx.doi.org/10.1198/tech.2001.s567.

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14

Lund, Robert. "Periodic Time Series Models." Journal of the American Statistical Association 100, no. 472 (December 2005): 1458–59. http://dx.doi.org/10.1198/jasa.2005.s50.

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15

Hongyi Li, G. S., and Maddala. "Bootstrapping time series models." Econometric Reviews 15, no. 2 (January 1996): 115–58. http://dx.doi.org/10.1080/07474939608800344.

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16

HUANG, YIJIAN, and LIMIN PENG. "Accelerated Recurrence Time Models." Scandinavian Journal of Statistics 36, no. 4 (December 2009): 636–48. http://dx.doi.org/10.1111/j.1467-9469.2009.00645.x.

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17

Martínez, Patricia López, Cesar Cuevas, and José M. Drake. "Compositional real-time models." Journal of Systems Architecture 58, no. 6-7 (June 2012): 257–76. http://dx.doi.org/10.1016/j.sysarc.2012.04.001.

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18

Tadić, Tvrtko. "Time-like Graphical Models." Memoirs of the American Mathematical Society 261, no. 1262 (September 2019): 0. http://dx.doi.org/10.1090/memo/1262.

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19

Micó, Joan C., Antonio Caselles, and Pantaleón D. Romero. "Space‐time dynamical models." Kybernetes 37, no. 7 (September 17, 2008): 1030–58. http://dx.doi.org/10.1108/03684920810884397.

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20

Gromov, N. A., and V. V. Kuratov. "Noncommutative space-time models." Czechoslovak Journal of Physics 55, no. 11 (November 2005): 1421–26. http://dx.doi.org/10.1007/s10582-006-0020-y.

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21

Ensor, Katherine Bennett. "Time series factor models." Wiley Interdisciplinary Reviews: Computational Statistics 5, no. 2 (February 7, 2013): 97–104. http://dx.doi.org/10.1002/wics.1245.

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22

Lipovina-Božović, Milena, Julija Cerovic, and Sasa Vujosević. "Forecasting inflation in Montenegro using univariate time series models." Business and Economic Horizons 11, no. 1 (March 15, 2015): 51–63. http://dx.doi.org/10.15208/beh.2015.05.

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23

Dias, Marco Antonio Guimarães, and José Paulo Teixeira. "Continuous-Time Option Games: Review of Models and Extensions." Multinational Finance Journal 14, no. 3/4 (December 1, 2010): 219–54. http://dx.doi.org/10.17578/14-3/4-3.

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24

Simeunovic, G., P. Zitek, and D. Lj Debeljkovic. "ICONE15-10352 TIME - DELAY MODELS OF HEAT TRANSFER SYSTEMS." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2007.15 (2007): _ICONE1510. http://dx.doi.org/10.1299/jsmeicone.2007.15._icone1510_177.

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25

Al-Hmouz, Rami, and Witold Pedrycz. "Models of time series with time granulation." Knowledge and Information Systems 48, no. 3 (January 16, 2016): 561–80. http://dx.doi.org/10.1007/s10115-015-0868-x.

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26

SÖDERSTROM, TORSTEN. "Computing stochastic continuous-time models from ARMA models." International Journal of Control 53, no. 6 (June 1991): 1311–26. http://dx.doi.org/10.1080/00207179108953677.

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27

Anupriya and Anita Singhrova. "Comparative Analysis of Time Series Forecasting Models for SDMN Traffic." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 531–40. http://dx.doi.org/10.5373/jardcs/v11/20192602.

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28

Broersen, P. M. T., and S. deWaele. "Automatic Identification of Time-Series Models From Long Autoregressive Models." IEEE Transactions on Instrumentation and Measurement 54, no. 5 (October 2005): 1862–68. http://dx.doi.org/10.1109/tim.2005.853232.

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29

Chen, Ming, Hao Wang, and Menglin Gong. "Discrete-time versus continuous-time toxic predation models." Journal of Difference Equations and Applications 28, no. 2 (February 1, 2022): 244–58. http://dx.doi.org/10.1080/10236198.2022.2038586.

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30

Izumi, Tomonori. "Discrete Time Models of Unilateral Time Delay Systems." IEEJ Transactions on Electronics, Information and Systems 129, no. 10 (2009): 1929–35. http://dx.doi.org/10.1541/ieejeiss.129.1929.

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31

Kiesel, Rüdiger, Magda Mroz, and Ulrich Stadtmüller. "Time-varying copula models for financial time series." Advances in Applied Probability 48, A (July 2016): 159–80. http://dx.doi.org/10.1017/apr.2016.48.

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AbstractWe perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we establish the limiting law of the maximum of the estimator series. Based on the limiting distributions, we develop statistical homogeneity tests, and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are an exception rather than the rule.
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32

Allan, Lorraine G., and Richard A. Block. "Cognitive Models of Psychological Time." American Journal of Psychology 105, no. 1 (1992): 140. http://dx.doi.org/10.2307/1422989.

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33

Jalali, Assad, and John Pemberton. "Mixture models for time series." Journal of Applied Probability 32, no. 1 (March 1995): 123–38. http://dx.doi.org/10.2307/3214925.

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In this paper we extend the class of zero-order threshold autoregressive models to a much richer class of mixture models. The new class has the important property of duality which, as we show, corresponds to time reversal. We are then able to obtain the time reversals of the zero-order threshold models and to characterise the time-reversible members of this subclass. These turn out to be quite trivial. The time-reversible models of the more general class do not suffer in this way. The complete stationary distributional structure is given, as are various moments, in particular the autocovariance function. This is shown to be of ARMA type. Finally we give two examples, the second of which extends from the finite to the countable mixture case. The general theory for this extension will be given elsewhere.
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34

Davies, Neville, and A. C. Harvey. "Time Series Models, 2nd Edn." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 1 (1995): 191. http://dx.doi.org/10.2307/2983424.

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35

Bohner, Martin, Julius Heim, and Ailian Liu. "SOLOW MODELS ON TIME SCALES." Cubo (Temuco) 15, no. 1 (March 2013): 13–32. http://dx.doi.org/10.4067/s0719-06462013000100002.

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36

Jentsch, Carsten, and Lena Reichmann. "Generalized Binary Time Series Models." Econometrics 7, no. 4 (December 14, 2019): 47. http://dx.doi.org/10.3390/econometrics7040047.

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The serial dependence of categorical data is commonly described using Markovian models. Such models are very flexible, but they can suffer from a huge number of parameters if the state space or the model order becomes large. To address the problem of a large number of model parameters, the class of (new) discrete autoregressive moving-average (NDARMA) models has been proposed as a parsimonious alternative to Markov models. However, NDARMA models do not allow any negative model parameters, which might be a severe drawback in practical applications. In particular, this model class cannot capture any negative serial correlation. For the special case of binary data, we propose an extension of the NDARMA model class that allows for negative model parameters, and, hence, autocorrelations leading to the considerably larger and more flexible model class of generalized binary ARMA (gbARMA) processes. We provide stationary conditions, give the stationary solution, and derive stochastic properties of gbARMA processes. For the purely autoregressive case, classical Yule–Walker equations hold that facilitate parameter estimation of gbAR models. Yule–Walker type equations are also derived for gbARMA processes.
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37

Baker, R. C. "Real-Time Inventory Cost Models." Journal of the Operational Research Society 38, no. 11 (November 1987): 1102. http://dx.doi.org/10.2307/2582243.

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38

Fung, K. Y., and Hongbin Ju. "Broadband Time-Domain Impedance Models." AIAA Journal 39, no. 8 (August 2001): 1449–54. http://dx.doi.org/10.2514/2.1495.

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39

Harvey, Andrew C. "Score-Driven Time Series Models." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 321–42. http://dx.doi.org/10.1146/annurev-statistics-040120-021023.

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The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range of disciplines. Their theoretical and practical advantages over other methods are highlighted. Topics covered include robust time series modeling, conditional heteroscedasticity, count data, dynamic correlation and association, censoring, circular data, and switching regimes.
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40

Billat, L. Vronique, J. Pierre Koralsztein, and R. Hugh Morton. "Time in Human Endurance Models." Sports Medicine 27, no. 6 (1999): 359–79. http://dx.doi.org/10.2165/00007256-199927060-00002.

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41

Gordon, Sheldon P., and Florence S. Gordon. "Mathematical Models of Waiting Time." Mathematics Teacher 83, no. 8 (November 1990): 622–27. http://dx.doi.org/10.5951/mt.83.8.0622.

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The old saying goes, “Time and tide wait for no man.” In today's society, an equally apt line might be, “Everyone waits for almost everything.” This widespread experience with waiting lends a marvelous opportunity to develop some very nice mathematics and apply it to problems with which our students can easily identify. In this art icle, we shall consider several mathematical models that can be used to study different waiting situations. The mathematics used involves just simple ideas from probability and mathematical expectation. Some related ideas are given in Mathers (1976). We shall also consider how computer simulations can be introduced to bring an added dimension to these topics. Most important, we shall see how such mathematical ideas furnish an ideal vehicle for involving students in actual individual research projects to collect real-life data, analyze it, and compare the results to predictions based on the mathematical model.
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42

Brody, Dorje C., and Lane P. Hughston. "Finite-time stochastic reduction models." Journal of Mathematical Physics 46, no. 8 (August 2005): 082101. http://dx.doi.org/10.1063/1.1990108.

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43

Alfa, Attahiru Sule. "Vacation Models in Discrete Time." Queueing Systems 45, no. 4 (December 2003): 381. http://dx.doi.org/10.1023/b:ques.0000018028.16682.ef.

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44

Stensholt, Boonchai K., and Dag Tjøstheim. "MULTIPLE BILINEAR TIME SERIES MODELS." Journal of Time Series Analysis 8, no. 2 (March 1987): 221–33. http://dx.doi.org/10.1111/j.1467-9892.1987.tb00434.x.

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45

McAleer, Michael, C. R. McKenzie, and A. D. Hall. "TESTING SEPARATE TIME SERIES MODELS." Journal of Time Series Analysis 9, no. 2 (March 1988): 169–89. http://dx.doi.org/10.1111/j.1467-9892.1988.tb00462.x.

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46

Tsay, Ruey S. "IDENTIFYING MULTIVARIATE TIME SERIES MODELS." Journal of Time Series Analysis 10, no. 4 (July 1989): 357–72. http://dx.doi.org/10.1111/j.1467-9892.1989.tb00034.x.

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47

Wall, Kent D., Christian Gourieroux, and Alain Monfort. "Time Series and Dynamic Models." Journal of the American Statistical Association 93, no. 443 (September 1998): 1248. http://dx.doi.org/10.2307/2669890.

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48

Hecht, Martin, Katinka Hardt, Charles C. Driver, and Manuel C. Voelkle. "Bayesian continuous-time Rasch models." Psychological Methods 24, no. 4 (August 2019): 516–37. http://dx.doi.org/10.1037/met0000205.

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49

Eberlein, E., and F. zkan. "Time consistency of Lévy models." Quantitative Finance 3, no. 1 (January 2003): 40–50. http://dx.doi.org/10.1088/1469-7688/3/1/304.

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50

Johansson, R. "Identification of continuous-time models." IEEE Transactions on Signal Processing 42, no. 4 (April 1994): 887–97. http://dx.doi.org/10.1109/78.285652.

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