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Journal articles on the topic 'Forecasting modeling'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Lummerzheim, Dirk. "Modeling and Forecasting Aurora." Computing in Science & Engineering 9, no. 5 (2007): 53–61. http://dx.doi.org/10.1109/mcse.2007.103.

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2

Chen, Rong. "Nonlinear modeling and forecasting." International Journal of Forecasting 9, no. 2 (1993): 273–74. http://dx.doi.org/10.1016/0169-2070(93)90012-c.

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3

KK, M. Casdagli, and S. Eubank. "Nonlinear Modeling and Forecasting: Proceedings of the Workshop on Nonlinear Modeling and Forecasting." Journal of the American Statistical Association 89, no. 428 (1994): 1569. http://dx.doi.org/10.2307/2291035.

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4

Aguzarova, Fatima S., and Agunda R. Tsirikhova. "MODELING AS A TAX FORECASTING TOOL." SOFT MEASUREMENTS AND COMPUTING 12, no. 73 (2023): 49–55. http://dx.doi.org/10.36871/2618-9976.2023.12.005.

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The scientific article examines the basics of modeling as a tool for tax forecasting. The authors provide definitions of the concepts of "tax planning" and "tax forecasting", proving that these are not identical terms. The characteristic of the levels of tax forecasting (macro and micro levels) is given. The researchers confirm that tax forecasting at each level is carried out by modeling various methods, the main of which are the method of expert assessments, simulation modeling, correlation and regression method, etc. The stages of the modeling process in taxation are considered. It is noted

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5

M.Thirunavukkarasu, M. Thirunavukkarasu, and A. Rajarathinam A.Rajarathinam. "Stochastic Modeling for Forecasting of India's Milled Rice Production." International Journal of Scientific Research 3, no. 1 (2012): 460–63. http://dx.doi.org/10.15373/22778179/jan2014/159.

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6

Onour, I. A., and B. S. Sergi. "Modeling and forecasting volatility in global food commodity prices." Agricultural Economics (Zemědělská ekonomika) 57, No. 3 (2011): 132–39. http://dx.doi.org/10.17221/28/2010-agricecon.

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To capture the volatility in the global food commodity prices, we employed two competing models, the thin tailed the normal distribution, and the fat-tailed Student t-distribution models. Results based on wheat, rice, sugar, beef, coffee, and groundnut prices, during the sample period from October 1984 to September 2009, show the t-distribution model outperforms the normal distribution model, suggesting that the normality assumption of residuals which are often taken for granted for its simplicity may lead to unreliable results of the conditional volatility estimates. The paper also shows that

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7

Roden, David B. "Forecasting Travel Time." Transportation Research Record: Journal of the Transportation Research Board 1518, no. 1 (1996): 7–12. http://dx.doi.org/10.1177/0361198196151800102.

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If travel time and speed are to be used as critical performance measures in congestion management systems and air quality analysis procedures, existing modeling techniques will need to be enhanced. Many of the simplifying assumptions that are built into traditional modeling techniques are described. Several relatively simple enhancements to existing models that can greatly improve the model's ability to estimate travel time and speeds are identified, and more advanced methods that could be considered as part of major model redevelopment efforts or detailed air quality studies are suggested. On

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8

Turniyaz, M. B., L. K. Nurshakhanova та S. T. Zakenov. "Problems оf Forecasting Modeling Mining". Oil and Gas Technologies 129, № 4 (2020): 25–28. http://dx.doi.org/10.32935/1815-2600-2020-129-4-25-28.

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The article presents the problems of construction, adaptation of hydrodynamic models and ensuring the most complete convergence of forecasting in the conditions of exploitation of deposits of Kazakhstan, which are at a late stage of development and suggests possible solutions to the identified problems.

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9

Wang, Lily, Guannan Wang, Xinyi Li, et al. "Modeling and Forecasting COVID-19." Notices of the American Mathematical Society 68, no. 04 (2021): 1. http://dx.doi.org/10.1090/noti2263.

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10

Tokarev, A. "Forecasting secessions though mathematical modeling." Pathways to Peace and Security, no. 2 (2021): 57–79. http://dx.doi.org/10.20542/2307-1494-2021-2-57-79.

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The article outlines and discusses the universal mathematical model created by the author and allowing to predict scenarios for post-Soviet secessions and, more broadly, to forecast secession potentials of any complex subnational regional units. The objects of forecasting are de facto states and different kinds of polities with failed statehood, analyzed through the prism of the “parent state – secession – patron state” triangle. The main research method is quantification of secession factors, which enables a researcher to measure the impacts of objective and subjective conditions on the cours

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11

Andersen, Torben G., Tim Bollerslev, Francis X. Diebold, and Paul Labys. "Modeling and Forecasting Realized Volatility." Econometrica 71, no. 2 (2003): 579–625. http://dx.doi.org/10.1111/1468-0262.00418.

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12

Cai, Zongwu, Yongmiao Hong, and Shouyang Wang. "Econometric Modeling and Economic Forecasting." Journal of Management Science and Engineering 3, no. 4 (2018): 178–82. http://dx.doi.org/10.3724/sp.j.1383.304010.

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13

Gudan, Jovita. "Modeling and Forecasting Exchange Rates." Lietuvos statistikos darbai 55, no. 1 (2016): 19–30. http://dx.doi.org/10.15388/ljs.2016.13864.

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This paper investigates models for the euro exchange rate against the currencies of Denmark, Poland, theUnited States, and the United Kingdom. The objective of this paper is to compare different methods of modeling andout-of-sample forecasting. One of the techniques is cointegration relation, which is implemented through a vector errorcorrection model. The existence of cointegration supports the long-run relationship between the nominal exchange rateand a number of fundamental variables. The evidence presented in this paper shows that a simple multivariate randomwalk model tends to have superi

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14

Lee, Ronald D., and Lawrence R. Carter. "Modeling and Forecasting U.S. Mortality." Journal of the American Statistical Association 87, no. 419 (1992): 659–71. http://dx.doi.org/10.1080/01621459.1992.10475265.

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15

Kulendran, Nada, and Kevin K. F. Wong. "Modeling Seasonality in Tourism Forecasting." Journal of Travel Research 44, no. 2 (2005): 163–70. http://dx.doi.org/10.1177/0047287505276605.

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16

Hunt, J. C. R. "Environmental forecasting and turbulence modeling." Physica D: Nonlinear Phenomena 133, no. 1-4 (1999): 270–95. http://dx.doi.org/10.1016/s0167-2789(99)00117-7.

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17

Mitchell, Daniel, Patrick Brockett, Rafael Mendoza-Arriaga, and Kumar Muthuraman. "Modeling and forecasting mortality rates." Insurance: Mathematics and Economics 52, no. 2 (2013): 275–85. http://dx.doi.org/10.1016/j.insmatheco.2013.01.002.

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18

Brito, B. O., R. M. Salgado, and L. A. Beijo. "Intelligent Modeling for Streamflow Forecasting." IEEE Latin America Transactions 14, no. 8 (2016): 3669–77. http://dx.doi.org/10.1109/tla.2016.7786349.

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19

Akimov, Valery. "Natural emergency modeling and forecasting." XXI century. Technosphere Safety 9, no. 1 (2024): 100–108. http://dx.doi.org/10.21285/2500-1582-2024-9-1-100-108.

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The purpose of this study is to develop forecast and analytical models (FAM) for predicting the most catastrophic natural emergencies caused by floods, earthquakes and forest fires. The article discusses predictive and analytical solutions for natural hazards for urbanized areas based on the Bayesian classifiers. The result is a formalized description of models for predicting forest fires, consequences of earthquakes and floods. The novelty of the models is due to the application of a unified scientific approach - the statistical processing method based on Bayes’ theorem. In contrast to the fr

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20

Latifa, Ghalayini, and Farhat Sara. "Modeling and Forecasting Gold Prices." Journal of Economics and Business 3, no. 4 (2020): 1708–29. https://doi.org/10.31014/aior.1992.03.04.314.

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The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate,

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21

Vinay, Kumar*. "TIME SERIES MODELING AND FORECASTING USING STOCHASTIC MODELS: A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 585–89. https://doi.org/10.5281/zenodo.205828.

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Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effeciency of time series modeling and forecasting. The aim of this paper is to present a concise description of some popular time series modeling and forecasting using stochastic models with their salient features.

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22

Thammawijaya, Panithee, and Rapeepong Suphanchaimat. "Accuracy of COVID-19 Prediction Modeling Techniques." Outbreak, Surveillance, Investigation & Response (OSIR) Journal 17, no. 3 (2024): 146–54. http://dx.doi.org/10.59096/osir.v17i3.270877.

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The unprecedented impact of the COVID-19 pandemic has revealed that forecasting capability is critically needed in making strategic decisions and formulating reasonable countermeasures. This study aimed to assess the predictive accuracy in forecasting the numbers of COVID-19 cases using Thailand’s national COVID-19 surveillance database from January 2020– June 2021 based on three analytical models: a susceptible-exposed-infectious-recovery compartmental model, an auto-regressive integrated moving average model, and a long short-term memory (LSTM) network model. All forecasting methods had mode

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23

Suh, Sang C., Sam I. Saffer, Dan Li, and Jingmiao Gao. "A NEW INSIGHT INTO PREDICTION MODELING SYSTEMS." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 8, no. 2 (2004): 85–104. http://dx.doi.org/10.3233/jid-2004-8208.

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Data mining and forecasting has attracted a lot of research interest in time series data sequence. The paper brings a new insight in how to select different time series forecasting models to make prediction according to different situations and data patterns. The contribution of this research is to facilitate prediction modelling system design for extensive forecasting purpose.

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24

薛, 浩. "Secondary Modeling of Air Quality Forecasting." Modeling and Simulation 11, no. 03 (2022): 648–62. http://dx.doi.org/10.12677/mos.2022.113061.

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25

Golosnoy, Vasyl, and Bastian Gribisch. "Modeling and forecasting realized portfolio weights." Journal of Banking & Finance 138 (May 2022): 106404. http://dx.doi.org/10.1016/j.jbankfin.2022.106404.

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26

Lee, Ronald D., and Lawrence R. Carter. "Modeling and Forecasting U. S. Mortality." Journal of the American Statistical Association 87, no. 419 (1992): 659. http://dx.doi.org/10.2307/2290201.

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27

McNown, Robert. "Modeling and Forecasting U.S. Mortality: Comment." Journal of the American Statistical Association 87, no. 419 (1992): 671. http://dx.doi.org/10.2307/2290202.

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28

Alho, Juha M. "Modeling and Forecasting U.S. Mortality: Comment." Journal of the American Statistical Association 87, no. 419 (1992): 673. http://dx.doi.org/10.2307/2290203.

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29

Lee, Ronald D., and Lawrence R. Carter. "Modeling and Forecasting U.S. Mortality: Rejoinder." Journal of the American Statistical Association 87, no. 419 (1992): 674. http://dx.doi.org/10.2307/2290204.

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30

Elishakoff, Issac. "Modeling Uncertainty in Flood Forecasting Systems." Shock and Vibration 13, no. 1 (2006): 63. http://dx.doi.org/10.1155/2006/703765.

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31

Borsch, S. V., Y. A. Simonov, and A. V. Khristoforov. "Efficiency of streamflow modeling and forecasting." Hydrometeorological research and forecasting, no. 1 (March 20, 2020): 176–89. http://dx.doi.org/10.37162/2618-9631-2020-1-176-189.

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32

De Mey, Pierre, Peter Craig, Fraser Davidson, et al. "APPLICATIONS IN COASTAL MODELING AND FORECASTING." Oceanography 22, no. 3 (2009): 198–205. http://dx.doi.org/10.5670/oceanog.2009.79.

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33

Sekine, Toshitaka. "Modeling and Forecasting Inflation in Japan." IMF Working Papers 01, no. 82 (2001): 1. http://dx.doi.org/10.5089/9781451850444.001.

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34

Callen, Tim, and Dongkoo Chang. "Modeling and Forecasting Inflation in India." IMF Working Papers 99, no. 119 (1999): 1. http://dx.doi.org/10.5089/9781451854152.001.

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35

Berhane, Tesfahun, Nurilign Shibabaw, Gurju Awgichew, and Tesfaye Kebede. "Modeling and Forecasting Rainfall in Ethiopia." International Journal of Computing Science and Applied Mathematics 4, no. 2 (2018): 42. http://dx.doi.org/10.12962/j24775401.v4i2.3824.

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36

Jai Sankar, T., R. Prabakaran, K. Senthamara, and S. Suresh. "Stochastic Modeling for Cattle Production Forecasting." Journal of Modern Mathematics and Statistics 4, no. 2 (2010): 53–57. http://dx.doi.org/10.3923/jmmstat.2010.53.57.

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37

Duda, Timothy F. "Modeling and Forecasting Ocean Acoustic Conditions." Journal of Marine Research 75, no. 3 (2017): 435–57. http://dx.doi.org/10.1357/002224017821836734.

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38

Žikeš, Filip, Jozef Baruník, and Nikhil Shenai. "Modeling and forecasting persistent financial durations." Econometric Reviews 36, no. 10 (2015): 1081–110. http://dx.doi.org/10.1080/07474938.2014.977057.

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39

Russo, Fabio, Federico Lombardo, Francesco Napolitano, and Eugenio Gorgucci. "Rainfall stochastic modeling for runoff forecasting." Physics and Chemistry of the Earth, Parts A/B/C 31, no. 18 (2006): 1252–61. http://dx.doi.org/10.1016/j.pce.2006.06.002.

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40

Ghalayini, Latife. "Modeling and forecasting spot oil price." Eurasian Business Review 7, no. 3 (2016): 355–73. http://dx.doi.org/10.1007/s40821-016-0058-0.

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41

Thomas, Barry. "Modeling and forecasting demand in tourism." Tourism Management 14, no. 3 (1993): 236–37. http://dx.doi.org/10.1016/0261-5177(93)90029-k.

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42

Gaines, Brian R. "Modeling and forecasting the information sciences." Information Sciences 57-58 (September 1991): 3–22. http://dx.doi.org/10.1016/0020-0255(91)90066-4.

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43

Lin, Winston T. "Modeling and forecasting U.S. public construction." International Journal of Forecasting 2, no. 3 (1986): 319–31. http://dx.doi.org/10.1016/0169-2070(86)90051-8.

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44

Mahmoud, Essam. "Modeling and forecasting demand in tourism." International Journal of Forecasting 8, no. 4 (1992): 643–44. http://dx.doi.org/10.1016/0169-2070(92)90078-n.

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45

Морозов, Михаил, Mikhail Morozov, Наталья Морозова, and Natalya Morozova. "Tourist destination development: Modeling and forecasting." Servis Plus 8, no. 3 (2014): 32–39. http://dx.doi.org/10.12737/5535.

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This article deals with the creation and development of the tourist destinations. The authors describe different types of development-related destination appeal, propose a logically interconnected sequence of stages in the formation of the desired profile of a tourist destination, and show that, in order to ensure effective functioning, it is imperative that economic and mathematical modeling to assess the potential of the current and achievable competitiveness of tourist destinations be used. The proposed model allows to calculate the quantitative effect of attracting investment to the develo

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46

Gür, İzzeddin, Mehmet Güvercin, and Hakan Ferhatosmanoglu. "Scaling forecasting algorithms using clustered modeling." VLDB Journal 24, no. 1 (2014): 51–65. http://dx.doi.org/10.1007/s00778-014-0363-0.

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47

Atamturktur, Sez, François Hemez, Brian Williams, Carlos Tome, and Cetin Unal. "A forecasting metric for predictive modeling." Computers & Structures 89, no. 23-24 (2011): 2377–87. http://dx.doi.org/10.1016/j.compstruc.2011.06.010.

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48

Ben Dhiab, Ali, Mehdi Ben Mimoun, Jose Oteros, et al. "Modeling olive-crop forecasting in Tunisia." Theoretical and Applied Climatology 128, no. 3-4 (2016): 541–49. http://dx.doi.org/10.1007/s00704-015-1726-1.

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49

Sadorsky, Perry. "Modeling and forecasting petroleum futures volatility." Energy Economics 28, no. 4 (2006): 467–88. http://dx.doi.org/10.1016/j.eneco.2006.04.005.

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50

Pulkkinen, Antti. "Geomagnetically Induced Currents Modeling and Forecasting." Space Weather 13, no. 11 (2015): 734–36. http://dx.doi.org/10.1002/2015sw001316.

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