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Relevant bibliographies by topics / Time-series analysis – Mathematical models

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Academic literature on the topic 'Time-series analysis – Mathematical models'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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Journal articles on the topic "Time-series analysis – Mathematical models"

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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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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 may differ only in nomenclature. ARMA-Transfer Function models can be used for systems which are linear or can be linearized such as typical BOD or suspended solids influent-effluent relationships for which the type of model is known. For systems which are highly nonlinear, and/or the input-output model is unknown, neural network models can be used. Both ARMA-TF models and neural network models can be made self-learning, that is, the performance of the model can be periodically improved manually or in an automated mode as new information is collected by monitoring. Application examples are included.

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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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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 time series. Thus, for the stiffness degrading systems and bilinear systems, the metrics of hysteretic energy demand and displacement ductility demand during displacement are used. The applicability of artificially generated acceleration time series for the qualitative description of information was shown. More specifically, ARMA (2,2) showed the best results in the study for three accelerated time series through nonlinear response analysis. In addition, as a result, normalized hysteretic energy demand, empirically valid displacement ductility relationships, and model parameters were proposed.

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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 of silk are also in the theoretic confidence interval, which can also verify the degree of accuracy and precision of the established model.

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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-propagation neural network (BPNN) model, and the data from July to December 2019 was used for validation. Results: A total of 30,083 rubella cases were reported in this study, with a significantly higher average annual incidence before the nationwide introduction of rubella-containing vaccine (RCV). The peak of rubella notification was from April to June annually. Both SARIMA and BPNN models were capable of predicting the expected incidence of rubella. However, the linear SARIMA model fits and predicts better than the nonlinear BPNN model. Conclusions: Based on the results, rubella incidence in Chongqing has an obvious seasonal trend, and SARIMA (2,1,1) × (1,1,1) 12 model can predict the incidence of rubella well. The SARIMA model is a feasible tool for producing reliable rubella forecasts in Chongqing.

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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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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, you should use the time series, which is influenced by the following factors: • Emerging trends of the series (the trend in cumulative long-term effects of many factors on the dynamics of the phenomenon under study - ascending or descending); • forming a series of cyclical fluctuations related to the seasonality of the disease; • random factors. In our study, we conducted a study to identify cyclical time series of long-term dynamics of morbidity of HFRS and autumn bank vole population. This study was performed using the autocorrelation coefficient. As a result of time-series studies of incidence of HFRS, indicators autumn bank vole population revealed no recurrence, and these figures are random variables, which is confirmed by three tests: nonrepeatability of time series, the assessment increase and decrease time-series analysis of the sum of squares. This shows that a number of indicators of the time series are random variables, contains a strong non-linear trend, to identify which need further analysis, for example by means of regression analysis.

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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 obtained results will help the decision makers to avoid unnecessary risk and correctly make a decision.

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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) model are connected with the ARIMA(p, d, q) orders of the describing each series separately. This step makes the set of the investigated ADL(p, q) models much smaller. In the previous papers it was also shown that the ARIMA(p, d, q) automatical fitting functions in popular packages use limitations on the p, q orders of the time series process: q ≤ 5, p ≤ 5. The wish to use the simplest models is also built in the structure of the Akaike (AIC) and Bayes (BIC) informational criteria. In the paper the maximal values of the ADL(p, q) model orders are supposed to be the orders of the appropriate ARIMA(p, d, q) series. In the previous work it was shown that using high order ARIMA(p, d, q) it is possible to fit the models better. In this paper the experiments on the ADL(p, q) models construction are presented. The wage index and money income index time series pair is researched, and also the gas, water and energy production and consumption index/real agricultural production index pair is investigated. The data in the 2000–2018 time period is taken from the dynamic series of macroeconomic statistics of the Russian Federation.

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Dissertations / Theses on the topic "Time-series analysis – Mathematical models"

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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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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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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 estimation. Finally, theoretical evaluation of the Fisher information matrix with missing observations is considered. The methodology is illustrated by applying the implied algorithm to real data. We consider modelling the white blood cell counts of a patient who has Leukaemia. Our modelling objective is to be able to describe the dynamic behaviour of the white blood cell counts.

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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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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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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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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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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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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 be described as “predictionist”. In the predictionist approach one builds models by attempting to answer the question, “given that the system is now here, where will it be next?” The quality of the model is judged by the degree to which the states of the model and the original system agree in the near future, conditioned on the present state of the model agreeing with that of the original system. Equivalently, the model is judged on its ability to make good short-term predictions on the original system. The main claim of this thesis is that prediction is often not the most appropriate criterion to apply when fitting models. We show, for example, that one can have models that, while able to make good predictions, have long term (or free-running) behaviour bearing little resemblance to that exhibited in the original time-series. We would hope to be able to use our models for a wide range of purposes other than just prediction — certainly we would like our models to exhibit good free-running behaviour. This thesis advocates a “behaviourist” approach, in which the criterion for a good model is that its long-term behaviour matches that exhibited by the data. We suggest that the behaviourist approach enjoys a certain robustness over the predictionist approaches. We show that good predictors can often be very poorly behaved, and suggest that well behaved models cannot perform too badly at the task of prediction. The thesis begins by comparing the predictionist and behaviourist approaches in the context of a number of simplified model-building problems. It then presents a simple theory for the understanding of the differences between the two approaches. Effective methods for the construction of well-behaved models are presented. Finally, these methods are applied to two real-world problems — modelling of the response of a voltage-clamped squid “giant” axon, and modelling of the “yearly sunspot number”.

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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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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 general specification for the correlation of the irregular, trend and seasonal components across the time series. Four structural time series models are presented, and the estimation of the components of the time series, as well as the estimation of the parameters which define this components, is discussed. The second part of the study deals with dynamic error components models where the irregular, trend and seasonal components are generated by common, as well as individual, effects. The extension to models for multivariate observations of cross-sections is also considered. Several applications of the methods studied are presented. Particularly relevant is an econometric study of the demand for energy in the U. K.

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Books on the topic "Time-series analysis – Mathematical models"

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

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

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

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

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

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

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

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

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

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

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Book chapters on the topic "Time-series analysis – Mathematical models"

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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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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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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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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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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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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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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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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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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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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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Conference papers on the topic "Time-series analysis – Mathematical models"

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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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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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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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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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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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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 estimated from the first minimum of the mutual information. The phase space reconstruction characteristics are then used to fully shape the architecture of a time delayed neural network model for the dynamical system. The modeling methodology is applied to several forced vibrating systems common to many fields of engineering. The neural models are then used to analyze new input, demonstrating the usefulness and importance of the methodology.

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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 relevant to mooring system design: Maximum Roll, Platform Offset and Fairlead Displacements. The meta-models are trained by real current, wind and wave data measured in 3h periods at the Campos Basin (Brazil) from 2003 to 2010 and the associated dynamic response of a spread-moored FPSO obtained through time-domain simulations using the Dynasim software. A comparative analysis of different model architectures is conducted and the proposed models are shown to correctly capture platform dynamics, providing good results when compared to the statistical analysis of time series motion obtained from Dynasim.

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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 and the efficiency of the simulations based on the state-space model representations are compared to those that integrate the convolutions.

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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 the roll damping. For each of the parameter variants, a numerical roll simulation is conducted and the according roll damping coefficients are determined by time series analysis. Finally, regression analysis is applied to the computed results in order to derive a mathematical model that allows to determine the roll damping coefficient depending on the hull shape parameter.

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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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Reports on the topic "Time-series analysis – Mathematical models"

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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 opportunity itself as in the ability to use them effectively. On the example of the e-learning course (ELC) “Mathematical Analysis” (Calculus) of Borys Grinchenko Kyiv University, which is developed on the basis of the Moodle platform, didactic and methodical approaches to content preparation and organization of activities in the ELC in mathematics are offered. Given the specifics of mathematics as a discipline, the possibility of using ELCs to support the traditional learning process with full-time learning is revealed, introducing a partially mixed (combined) model. It is emphasized that effective formation of mathematical competence of students by means of e-learning is possible only in the conditions of research-oriented educational environment with active and concerned participation of students and partnership interaction. The prospect of further research in the analysis of e-learning opportunities for the formation of students’ mathematical competence, in particular, research and investigation tools, and the development of recommendations for the advanced training programs of teachers of mathematical disciplines of universities are outlined.

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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 the values of parameters without taking into account their real physical properties. The correctness of the final values will be based on dynamically selected parameters, which allows you to modify the terms of use of the system in real time. In the development process, the values of experimentally obtained data with the model are compared, which allows you to understand the accuracy of minimization. When choosing the most relevant parameters, various minimization functions are used, which provides an opportunity to cover a wide range of theoretical initial situations. Verification of the correctness of the decision is carried out with the help of a quality function, which can identify the accuracy and correctness of the optimized parameters. It is possible to choose different types of functional quality, depending on the characteristics of the initial data. The presence of such tools during parametrization allows for varied analysis of the model, testing it on various algorithms, data volumes and conditions of guaranteed convergence of functional methods.

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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 transport techniques for different crops and structure types. All the objectives have been successfully addressed. The study was focused on both screenhouses and naturally ventilated greenhouses, and all proposed methods were examined. Background to the topic Our previous BARD project established that the eddy covariance (EC) technique is suitable for whole canopy evapotranspiration measurements in large agricultural screenhouses. Nevertheless, the eddy covariance technique remains difficult to apply in the farm due to costs, operational complexity, and post-processing of data – thereby inviting alternative techniques to be developed. The subject of this project was: 1) the evaluation of four turbulent transport (TT) techniques, namely, Surface Renewal (SR), Flux-Variance (FV), Half-order Time Derivative (HTD) and Bowen Ratio (BR), whose instrumentation needs and operational demands are not as elaborate as the EC, to estimate evapotranspiration within large agricultural structures; and 2) the development of mathematical models able to predict water savings and account for the external environmental conditions, physiological properties of the plant, and structure properties as well as to evaluate the necessary micrometeorological conditions for utilizing the above turbulent transfer methods in such protected environments. Major conclusions and achievements The major conclusions are: (i) the SR and FV techniques were suitable for reliable estimates of ET in shading and insect-proof screenhouses; (ii) The BR technique was reliable in shading screenhouses; (iii) HTD provided reasonable results in the shading and insect proof screenhouses; (iv) Quality control analysis of the EC method showed that conditions in the shading and insect proof screenhouses were reasonable for flux measurements. However, in the plastic covered greenhouse energy balance closure was poor. Therefore, the alternative methods could not be analyzed in the greenhouse; (v) A multi-layered flux footprint model was developed for a ‘generic’ crop canopy situated within a protected environment such as a large screenhouse. The new model accounts for the vertically distributed sources and sinks within the canopy volume as well as for modifications introduced by the screen on the flow field and microenvironment. The effect of the screen on fetch as a function of its relative height above the canopy is then studied for the first time and compared to the case where the screen is absent. The model calculations agreed with field experiments based on EC measurements from two screenhouse experiments. Implications, both scientific and agricultural The study established for the first time, both experimentally and theoretically, the use of four simple TT techniques for ET estimates within large agricultural screenhouses. Such measurements, along with reliable theoretical models, will enable the future development of lowcost ET monitoring system which will be attainable for day-to-day use by growers in improving irrigation management.

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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). Relativistic quantum mechanics rejects the existence of the immediate values of any physical quantity in principle, and consequently the notion of the system state, including the notion of the wave function, which becomes rigorously nondeﬁnable. The task of this work consists in econophysical analysis of the conceptual fundamentals and mathematical apparatus of the classical physics, relativity theory, non-relativistic and relativistic quantum mechanics, subject to the historical, psychological and philosophical aspects and modern state of the socio-economic modeling problem. We have shown that actually and, virtually, a long time ago, new paradigms of modeling were accepted in the quantum theory, within the bounds of which the notion of the physical quantity operator becomes the primary fundamental conception(operator is a mathematical image of the procedure, the action), description of the system dynamics becomes discrete and approximate in its essence, prediction of the future, even in the rough, is actually impossible when setting aside the aftereﬀect i.e. the memory. In consideration of the analysis conducted in the work we suggest new paradigms of the economical-mathematical modeling.

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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 concept of modeling and technological integration of various organizational forms and teaching methods based on the concept of mobility. Within this approach, the leading learning tools of bachelors of electromechanics are mobile Internet devices (MID) – a multimedia mobile devices that provide wireless access to information and communication Internet services for collecting, organizing, storing, processing, transmitting, presenting all kinds of messages and data. The authors reveals the main possibilities of using MID in learning to ensure equal access to education, personalized learning, instant feedback and evaluating learning outcomes, mobile learning, productive use of time spent in classrooms, creating mobile learning communities, support situated learning, development of continuous seamless learning, ensuring the gap between formal and informal learning, minimize educational disruption in conflict and disaster areas, assist learners with disabilities, improve the quality of the communication and the management of institution, and maximize the cost-efficiency. Bachelor of electromechanics competency in modeling of technical objects is a personal and vocational ability, which includes a system of knowledge, skills, experience in learning and research activities on modeling mechatronic systems and a positive value attitude towards it; bachelor of electromechanics should be ready and able to use methods and software/hardware modeling tools for processes analyzes, systems synthesis, evaluating their reliability and effectiveness for solving practical problems in professional field. The competency structure of the bachelor of electromechanics in the modeling of technical objects is reflected in three groups of competencies: general scientific, general professional and specialized professional. The implementation of the technique of using MID in learning bachelors of electromechanics in modeling of technical objects is the appropriate methodic of using, the component of which is partial methods for using MID in the formation of the general scientific component of the bachelor of electromechanics competency in modeling of technical objects, are disclosed by example academic disciplines “Higher mathematics”, “Computers and programming”, “Engineering mechanics”, “Electrical machines”. The leading tools of formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects are augmented reality mobile tools (to visualize the objects’ structure and modeling results), mobile computer mathematical systems (universal tools used at all stages of modeling learning), cloud based spreadsheets (as modeling tools) and text editors (to make the program description of model), mobile computer-aided design systems (to create and view the physical properties of models of technical objects) and mobile communication tools (to organize a joint activity in modeling).

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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 traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.

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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 season. An accurate forecast of the volume of water on the seasonal scale is necessary for better planning of the structure of crops, and subsequently water use in the irrigation of crops. An acceptable solution to this challenge is the construction of an empirical time series model that will be used to predict the seasonal flows of the Amu Darya River to improve the planning and management of water resources in downstream countries. This article considers three important discharge time series in the larger Amu Darya Basin. These include the Kerki Gauge on the Amu Darya, Darband Gauge on Vaksh River and Khorog Gauge on Gunt River. Long-term time series from these stations are available for the study of the development and implementation of time-series based models for the prediction of discharge in the basin. At this stage, we attempt to demonstrate a proof-of-concept which can in a second step convince stakeholders to share such type of discharge data operationally for more effective water allocation between sectors and countries. All our work was carried out with the quantitative tools R/RStudio and QGIS. It can serve as a stepping stone for more complex forecasting models in the future.

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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 selected tools is given. Criteria for the selection of such tools were identified – functional-didactic and organizational. The following indicators belong to the functional-didactic: the presence of different types of questions, including open-ended; use of formulas, both in questions and in answers; use of pictures, both in questions and in answers; no restrictions on the length of questions and answers; instant receipt of results by the teacher, their evaluation and analysis; instant receipt of results by the respondent; to the organizational: the availability of a free version; no need to install the program; ease of use – characterizes the convenience and clarity of the interface for creating tests and their use; possibility of testing in online and offline mode; time limits, both for a single question and the whole test; random order of questions/answer options; instant demonstration of the correct answer to the respondent. With the help of expert evaluation, it was found that according to these criteria, Quizizz is the most appropriate for testing.

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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-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.

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