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Добірка наукової літератури з теми "Time-series analysis – Mathematical models"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 31 січня 2023

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Статті в журналах з теми "Time-series analysis – Mathematical models"

1

Petrusevich, D. A. "ANALYSIS OF MATHEMATICAL MODELS USED FOR ECONOMETRICAL TIME SERIES FORECASTING." Russian Technological Journal 7, no. 2 (May 16, 2019): 61–73. http://dx.doi.org/10.32362/2500-316x-2019-7-2-61-73.

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2

Novotny, V., H. Jones, X. Feng, and A. Capodaglio. "Time Series Analysis Models of Activated Sludge Plants." Water Science and Technology 23, no. 4-6 (February 1, 1991): 1107–16. http://dx.doi.org/10.2166/wst.1991.0562.

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Time series models of the activated sludge process are very useful in design and real time operation of wastewater treatment systems which deal with variable influent flows and pollution loads. In contrast to common deterministic dynamic mathematical models which require knowledge of a large number of coefficients, the time series models can be developed from input and output monitoring data series. In order to avoid “black box” approaches, time series models can be made compatible and identical in principle, with their dynamic mass balance model equivalents. In fact, these two types of models

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3

Ray, Bonnie K. "Regression Models for Time Series Analysis." Technometrics 45, no. 4 (November 2003): 364. http://dx.doi.org/10.1198/tech.2003.s166.

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4

Brahimi, Tahar, and Tahar Smain. "A Nonstationary Mathematical Model for Acceleration Time Series." Mathematical Modelling of Engineering Problems 8, no. 2 (April 28, 2021): 246–52. http://dx.doi.org/10.18280/mmep.080211.

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The choice of nonstationary stochastic models for the study is fully justified by the limitation of acceleration time series number. The three acceleration time series under consideration are used to generate a new, artificial series of ten per historical one using autoregressive moving average model. Subsequently, the average of nonlinear is utilized for the ten acceleration time series in order to obtain the spectral response of a system with single degree of freedom. Modeling of acceleration time series involves critical estimation of metrics that characterize nonstationary acceleration tim

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5

Yang, Xi, Bo Nie, Bing Di Liu, Hai Liu, and Lun Bai. "Time Series Modeling and Analysis on the Silk Crape Satin Product." Advanced Materials Research 175-176 (January 2011): 412–17. http://dx.doi.org/10.4028/www.scientific.net/amr.175-176.412.

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Empirical analysis on typical product categories, product series and Price Index of every level of single species is made by using classical ARMA models as well as ARCH models, which based on the actual data sampling and network. This study sets up AR models with ARCH effect of timing of product operations Index that judged by LM test used as model identification, and then establishes corresponding mathematical quantitative model for prediction. All of these are carried out by the Metrical Economics and the Eviews software. With time series, the fitting and prediction for running change-trend

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6

Chen, Qi, Han Zhao, Hongfang Qiu, Qiyin Wang, Dewei Zeng, and Mengliang Ye. "Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models." Journal of Infection in Developing Countries 16, no. 08 (August 30, 2022): 1343–50. http://dx.doi.org/10.3855/jidc.16475.

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Introduction: Chongqing is among the areas with the highest rubella incidence rates in China. This study aimed to analyze the temporal distribution characteristics of rubella and establish a forecasting model in Chongqing, which could provide a tool for decision-making in the early warning system for the health sector. Methodology: The rubella monthly incidence data from 2004 to 2019 were obtained from the Chongqing Center of Disease and Control. The incidence from 2004 to June 2019 was fitted using the seasonal autoregressive integrated moving average (SARIMA) model and the back-propaga

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7

Gluhovsky, Alexander, and Kevin Grady. "Effective low-order models for atmospheric dynamics and time series analysis." Chaos: An Interdisciplinary Journal of Nonlinear Science 26, no. 2 (February 2016): 023119. http://dx.doi.org/10.1063/1.4942586.

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8

Евстегнеева, V. Evstegneeva, Честнова, Tatyana Chestnova, Смольянинова, and O. Smolyaninova. "Time series analysis in forecasting pririrodno focal infections." Journal of New Medical Technologies. eJournal 9, no. 4 (December 8, 2015): 0. http://dx.doi.org/10.12737/17087.

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Mathematical methods and models used in forecasting problems may relate to a wide variety of topics: from the regression analysis, time series analysis, formulation and evaluation of expert opinions, simulation, systems of simultaneous equations, discriminant analysis, logit and probit models, logical unit decision functions, variance or covariance analysis, rank correlation and contingency tables, etc. In the analysis of the phenomenon over a long timeperiod, for example, the incidence of long-term dynamics with a forecast of further development of the process, y

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9

Sidorova, N. Р., and D. S. Demina. "Comparison of results of forecasting of time series based on autoregression analysis and model trends." Informacionno-technologicheskij vestnik 13, no. 3 (September 30, 2017): 118–26. http://dx.doi.org/10.21499/2409-1650-2017-3-118-126.

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At the moment, there are various forecasting models. Model tendencies are based on the key technical analysis techniques: smoothing data using a mathematical average, the allocation trend. Attempt selecting optimal models, are showing the minimum average error of prediction. On the basis of autoregressive models, based on the sample maximum likelihood, and model trends based on the methods of technical analysis based forecast for a sufficiently long period. Thus, the proposed models give a forecast with minimum average error, and its values are in the interval allowed for the researcher. The o

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10

Kalugin, T. R., A. K. Kim, and D. A. Petrusevich. "Analysis of the high order ADL(p, q) models used to describe connections between time series." Russian Technological Journal 8, no. 2 (April 14, 2020): 7–22. http://dx.doi.org/10.32362/2500-316x-2020-8-2-7-22.

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In the paper the mathematical models describing connection between two time series are researched. At first each of them is investigated separately, and the ARIMA(p, d, q) model is constructed. These models are based on the time series characteristics obtained during the analysis stage. The connection between two time series is confirmed with the aid of cointegration statistical tests. Then the mathematical model of the connection between series is constructed. The ADL(p, q) model describes this dependence. It’s shown that for the time series under investigation the orders p, q of the ADL(p, q

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Дисертації з теми "Time-series analysis – Mathematical models"

1

黃鎮山 and Chun-shan Wong. "Statistical inference for some nonlinear time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31239444.

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2

Wong, Chun-shan. "Statistical inference for some nonlinear time series models /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20715316.

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3

Cheung, King Chau. "Modelling multiple time series with missing observations." Thesis, Canberra, ACT : The Australian National University, 1993. http://hdl.handle.net/1885/133887.

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This thesis introduces an approach to the state space modelling of time series that may possess missing observations. The procedure starts by estimating the autocovariance sequence using an idea proposed by Parzen(1963) and Stoffer(1986). Successive Hankel matrices are obtained via Autoregressive approximations. The rank of the Hankel matrix is determined by a singular value decomposition in conjunction with an appropriate model selection criterion . An in tern ally balanced state space realisation of the selected Hankel matrix provides initial estimate for maximum likelihood estimati

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4

Jin, Shusong, and 金曙松. "Nonlinear time series modeling with application to finance and other fields." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B3199605X.

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5

Chan, Yin-ting, and 陳燕婷. "Topics on actuarial applications of non-linear time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B32002099.

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6

Lin, Zhongli, and 林中立. "On the statistical inference of some nonlinear time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43757625.

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7

Yiu, Fu-keung, and 饒富強. "Time series analysis of financial index." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267804.

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8

Kilminster, Devin. "Modelling dynamical systems via behaviour criteria." University of Western Australia. Dept. of Mathematics and Statistics, 2002. http://theses.library.uwa.edu.au/adt-WU2003.0029.

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Анотація:

An important part of the study of dynamical systems is the fitting of models to time-series data. That is, given the data, a series of observations taken from a (not fully understood) system of interest, we would like to specify a model, a mathematical system which generates a sequence of “simulated” observations. Our aim is to obtain a “good” model — one that is in agreement with the data. We would like this agreement to be quantitative — not merely qualitative. The major subject of this thesis is the question of what good quantitative agreement means. Most approaches to this question could b

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9

Thyer, Mark Andrew. "Modelling long-term persistence in hydrological time series." Diss., 2000, 2000. http://www.newcastle.edu.au/services/library/adt/public/adt-NNCU20020531.035349/index.html.

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10

Rivera, Pablo Marshall. "Analysis of a cross-section of time series using structural time series models." Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/13/.

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This study deals with multivariate structural time series models, and in particular, with the analysis and modelling of cross-sections of time series. In this context, no cause and effect relationships are assumed between the time series, although they are subject to the same overall environment. The main motivations in the analysis of cross-sections of time series are (i) the gains in efficiency in the estimation of the irregular, trend and seasonal components; and (ii) the analysis of models with common effects. The study contains essentially two parts. The first one considers models with a

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Книги з теми "Time-series analysis – Mathematical models"

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

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

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5

Gourieroux, Christian. Time series and dynamic models. Cambridge: Cambridge University Press, 1997.

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6

Gourieroux, Christian. Time series and dynamic models. New York: Cambridge University Press, 1997.

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7

Lewis, Peter A. W. Some simple models for continuous variate time series. Monterey, Calif: Naval Postgraduate School, 1985.

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8

Mueller, Uli. Testing models of low-frequency variability. Cambridge, Mass: National Bureau of Economic Research, 2006.

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9

Brockwell, Peter J. Time series: Theory and methods. 2nd ed. New York: Springer, 1996.

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10

A, Davis Richard, ed. Time series: Theory and methods. New York: Springer-Verlag, 1987.

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Частини книг з теми "Time-series analysis – Mathematical models"

1

Enright, Catherine G., Michael G. Madden, Niall Madden, and John G. Laffey. "Clinical Time Series Data Analysis Using Mathematical Models and DBNs." In Artificial Intelligence in Medicine, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22218-4_20.

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2

Lai, Tze Leung, and Samuel Po-Shing Wong. "Combining domain knowledge and statistical models in time series analysis." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 193–209. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2006. http://dx.doi.org/10.1214/074921706000001049.

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3

Gupta, Mohit, Ayushi Asthana, Nishant Joshi, and Pulkit Mehndiratta. "Improving Time Series Forecasting Using Mathematical and Deep Learning Models." In Big Data Analytics, 115–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04780-1_8.

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4

Layton, W., and M. McLaughlin. "On URANS Congruity with Time Averaging: Analytical Laws Suggest Improved Models." In Mathematical Analysis With Applications, 85–108. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42176-2_10.

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5

Eltoft, Torbjørn, Laurent Ferro-Famil, Stian N. Anfinsen, and Anthony P. Doulgeris. "Polarimetric SAR Modelling: Mellin Kind Statistics and Time-Frequency Analysis." In Mathematical Models for Remote Sensing Image Processing, 191–242. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66330-2_5.

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6

Pavlova, N. G. "Applications of the Theory of Covering Maps to the Study of Dynamic Models of Economic Processes with Continuous Time." In Mathematical Analysis With Applications, 123–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42176-2_12.

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7

Newman, W. I., and Y. Terzian. "Power Spectrum Analysis and Redshift Data." In Modern Mathematical Models of Time and their Applications to Physics and Cosmology, 127–41. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5628-8_11.

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8

Dhillon, B. S. "Mathematical Models for Performing Human Reliability and Error Analysis in Power Plants." In Springer Series in Reliability Engineering, 151–68. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04019-6_11.

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9

Goyal, Manish, Amit Prakash, and Shivangi Gupta. "Numerical Analysis of Coupled Time-Fractional Differential Equations Arising in Epidemiological Models." In Mathematical Modeling and Soft Computing in Epidemiology, 173–98. First edition. | Boca Raton, FL : CRC Press, 2021. |: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038399-9.

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10

Sutradhar, Brajendra C. "On cluster regression and factor analysis models with elliptic $t$ errors." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 369–83. Hayward, CA: Institute of Mathematical Statistics, 1994. http://dx.doi.org/10.1214/lnms/1215463809.

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Тези доповідей конференцій з теми "Time-series analysis – Mathematical models"

1

Freudenthaler, Christoph, Steffen Rendle, Lars Schmidt-Thieme, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Factorizing Markov Models for Categorical Time Series Prediction." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636749.

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2

Abdulkadir, Said Jadid, and Suet-Peng Yong. "Lorenz time-series analysis using a scaled hybrid model." In 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC). IEEE, 2015. http://dx.doi.org/10.1109/ismsc.2015.7594082.

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3

Çekim, Hatice Ö, Cem Kadılar, and Gamze Özel. "Characterizing forest fire activity in Turkey by compound Poisson and time series models." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4825789.

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4

Gonçalves, A. Manuela, Olexandr Baturin, and Marco Costa. "Time series analysis by state space models applied to a water quality data in Portugal." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5044171.

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5

Sestelo, Marta, Nora M. Villanueva, Javier Roca-Pardiñas, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Selecting Variables in Regression Models. A New Approach to the Prediction of Time Series of S O[sub 2]." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637003.

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6

Zolock, John, and Robert Greif. "Application of Time Series Analysis and Neural Networks to the Modeling and Analysis of Forced Vibrating Mechanical Systems." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-55519.

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A theoretical and mathematical based methodogy is discussed that utilizes time series analysis techniques and neural networks to model forced vibrating mechanical systems using measured input-output data. A technique in nonlinear time series analysis known as phase space reconstruction may be used to extend our understanding of the active dynamics recorded in a single time series measurement. Using a recorded output (response) measurement phase space reconstruction parameters are calculated; the embedding dimension is estimated using the method of false nearest neighbor, and the time delay is

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7

Cotrim, Lucas Pereira, Henrique Barros Oliveira, Asdrubal N. Queiroz Filho, Ismael H. F. Santos, Rodrigo Augusto Barreira, Eduardo Aoun Tannuri, Anna Helena Reali Costa, and Edson Satoshi Gomi. "Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-62674.

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Abstract The current design process of mooring systems for FPSOs is highly dependent on the availability of the platform’s mathematical model and accuracy of dynamic simulations, through which resulting time series motion is evaluated according to design constraints. This process can be time-consuming and present inaccurate results due to the mathematical model’s limitations and overall complexity of the vessel’s dynamics. We propose a Neural Simulator, a set of data-based surrogate models with environmental data as input, each specialized in the prediction of different motion statistics relev

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8

Taghipour, Reza, Tristan Perez, and Torgeir Moan. "Time Domain Hydroelastic Analysis of a Flexible Marine Structure Using State-Space Models." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29272.

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This article deals with time-domain hydroelastic analysis of a marine structure. The convolution terms in the mathematical model are replaced by their alternative state-space representations whose parameters are obtained by using the realization theory. The mathematical model is validated by comparison to experimental results of a very flexible barge. Two types of time-domain simulations are performed: dynamic response of the initially inert structure to incident regular waves and transient response of the structure after it is released from a displaced condition in still water. The accuracy a

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9

Piehl, Henry, and Ould el Moctar. "A Mathematical Model for Roll Damping Prediction." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41642.

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A ship in seaway is always prone to roll motion. For the safety of personnel, ship and cargo it is essential to optimize the roll damping properties of the hull shape in order to prevent exceeding roll angles. Therefore, a tool for the prediction of roll damping is an important requirement during the design phase of ship hulls. The objective of this study is to use regression analysis and numerical simulation of roll motion to develop an analytic expression for the determination of roll damping. The development procedure starts with a variation of several hull shape parameter that influence th

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10

Schmidt, Michael D., and Hod Lipson. "Data-Mining Dynamical Systems: Automated Symbolic System Identification for Exploratory Analysis." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59309.

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This paper describes a new algorithm for automatically reverse-engineering symbolic analytical models of dynamical systems directly from experimental observations, for the purpose of modeling, control and exploratory analysis. The new algorithm builds on genetic programming techniques used in symbolic regression to infer differential equations from time series data. We introduce the core algorithm for building coherent mathematical models efficiently and then describe its application to system identification. The method is demonstrated on a number of nonlinear mechanical and biological systems

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Звіти організацій з теми "Time-series analysis – Mathematical models"

1

Astafieva, Mariia M., Oleksii B. Zhyltsov, and Volodymyr V. Proshkin. E-learning as a mean of forming students' mathematical competence in a research-oriented educational process. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3896.

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The article is devoted to the substantiation of approaches to the effective use of advantages and minimization of disadvantages and losses of e-learning as a mean of forming mathematical competence of students in the conditions of research-oriented educational process. As a result of the ascertaining experiment, e-learning has certain disadvantages besides its obvious advantages (adaptability, possibility of individualization, absence of geographical barriers, ensuring social equality, unlimited number of listeners, etc.). However, the nature of these drawbacks lies not as much in the plane of

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2

Eichenbaum, Martin, Lars Peter Hansen, and Kenneth Singleton. A Time Series Analysis of Representative Agent Models of Consumption andLeisure Choice Under Uncertainty. Cambridge, MA: National Bureau of Economic Research, July 1986. http://dx.doi.org/10.3386/w1981.

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Kuropiatnyk, D. I. Actuality of the problem of parametric identification of a mathematical model. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2885.

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The purpose of the article is to study the possibilities of increasing the efficiency of a mathematical model by identifying the parameters of an object. A key factor for parametrization can be called the consideration of properties of the values of the model at a specific time point, which allows a deeper analysis of data dependencies and correlation between them. However, such a technique does not always work, because in advance it is impossible to predict that the parameters can be substantially optimized. In addition, it is necessary to take into account the fact that minimization reduces

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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Micrometeorological methods for inferring whole canopy evapotranspiration in large agricultural structures: measurements and modeling. United States Department of Agriculture, October 2015. http://dx.doi.org/10.32747/2015.7594402.bard.

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Original objectives and revisions The original objectives as stated in the approved proposal were: (1) To establish guidelines for the use of micrometeorological techniques as accurate, reliable and low-cost tools for continuous monitoring of whole canopy ET of common crops grown in large agricultural structures. (2) To adapt existing methods for protected cultivation environments. (3) To combine previously derived theoretical models of air flow and scalar fluxes in large agricultural structures (an outcome of our previous BARD project) with ET data derived from application of turbulent transp

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Saptsin, Vladimir, and Володимир Миколайович Соловйов. Relativistic quantum econophysics – new paradigms in complex systems modelling. [б.в.], July 2009. http://dx.doi.org/10.31812/0564/1134.

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This work deals with the new, relativistic direction in quantum econophysics, within the bounds of which a change of the classical paradigms in mathematical modelling of socio-economic system is oﬀered. Classical physics proceeds from the hypothesis that immediate values of all the physical quantities, characterizing system’s state, exist and can be accurately measured in principle. Non-relativistic quantum mechanics does not reject the existence of the immediate values of the classical physical quantities, nevertheless not each of them can be simultaneously measured (the uncertainty principle

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Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.

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An analysis of the experience of professional training bachelors of electromechanics in Ukraine and abroad made it possible to determine that one of the leading trends in its modernization is the synergistic integration of various engineering branches (mechanical, electrical, electronic engineering and automation) in mechatronics for the purpose of design, manufacture, operation and maintenance electromechanical equipment. Teaching mechatronics provides for the meaningful integration of various disciplines of professional and practical training bachelors of electromechanics based on the concep

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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditio

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Shadurdyyev, G. Analysis of sets of factors affecting the variable flow of the Amu Darya River to create a seasonal prognostic model. Kazakh-German University, December 2022. http://dx.doi.org/10.29258/dkucrswp/2022/53-72.eng.

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The Amu Darya River is a transboundary river whose flow of the river in high-water years reaches up to 108 km3 and in low-water years up to 47 km3 and these are huge fluctuations in the water flow of the river for Tajikistan, Kyrgyzstan, Uzbekistan, Turkmenistan, and Afghanistan, that share water among themselves. The point to consider is that the downstream countries Turkmenistan and Uzbekistan (and possibly Afghanistan in the future) use a lot of water for irrigation, and therefore these countries are the ones most in need of an accurate forecast of the volume of water for the upcoming seaso

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Zaika, Oksana V., Tetiana A. Vakaliuk, Andrii V. Riabko, Roman P. Kukharchuk, Iryna S. Mintii, and Serhiy O. Semerikov. Selection of online tools for creating math tests. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4594.

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The article considers online tools for creating tests, which should be used when teaching mathematics in both higher education and general secondary education. Among the variety of online means of creating tests by the method of expert evaluation, three were identified, which allow conducting various tests both in the classroom and remotely, which are free and do not require special conditions for their use and which work on smartphones. The advantages and disadvantages of three online tools for creating tests Kahoot!, Quizizz, Classtime are analyzed, and a comparative description of the selec

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Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-ba

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