Academic literature on the topic 'Forecasting modeling'

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Journal articles on the topic "Forecasting modeling"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Forecasting modeling"

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Marsilli, Clément. "Mixed-Frequency Modeling and Economic Forecasting." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2023/document.

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La prévision macroéconomique à court terme est un exercice aussi complexe qu’essentiel pour la définition de la politique économique et monétaire. Les crises financières récentes ainsi que les récessions qu’ont endurées et qu’endurent aujourd’hui encore, en ce début d’année 2014, nombre de pays parmi les plus riches, témoignent de la difficulté d’anticiper les fluctuations économiques, même à des horizons proches. Les recherches effectuées dans le cadre de la thèse de doctorat qui est présentée dans ce manuscrit se sont attachées à étudier, analyser et développer des modélisations pour la prév
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Xu, Huaidong. "Forecasting innovation diffusion : a modeling approach." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/23413.

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Ning, Yue. "Modeling Information Precursors for Event Forecasting." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84486.

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This dissertation is focused on the design and evaluation of machine learning algorithms for modeling information precursors for use in event modeling and forecasting. Given an online stream of information (e.g., news articles, social media postings), how can we model and understand how events unfold, how they influence each other, and how they can act as determinants of future events? First, we study information reciprocity in joint news and social media streams to capture how events evolve. We present an online story chaining algorithm which links related news articles together in a low com
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Shah, Ismail. "Modeling and Forecasting Electricity Market Variables." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3427110.

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In deregulated electricity markets, accurate modeling and forecasting of different variables, e.g. demand, prices, production etc. have obtained increasing importance in recent years. As in most electricity markets, the daily demand and prices are determined the day before the physical delivery by means of (semi-) hourly concurrent auctions, accurate forecasts are necessary for the efficient management of power systems. However, it is well known that electricity (demand/price) data exhibit some specific features, among which, daily, weekly and annual periodic patterns as well as non-constant m
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Ahmed, Shadman. "Phase-Out Demand Forecasting : Predictive modeling on forecasting product life cycle." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287446.

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The phase-out stage in a product life cycle can face unpredictable demand. Accurate forecast of the phase-out demand can help supply chain managers to control the number of obsolete inventories. Consequently, having a positive effect in terms of resources and lower scrap costs. In this thesis, we investigated if data-driven forecasting models could improve the accuracy of forecasting the phase-out stage when compared with domain experts. Since the space of available models is vast, a set of 11 best performing models according to literature were investigated. Furthermore, a thorough model selec
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Brendel, Conrad. "Analysis, Modeling, and Forecasting Of Urban Flooding." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/105131.

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As the world becomes more urbanized and heavy precipitation events increase in frequency and intensity, urban flooding is an emerging concern. Urban flooding is caused when heavy rainfall collects on the landscape, exceeding the capacity of drainage systems to effectively convey runoff. Unlike riverine and coastal flooding, urban flooding occurs frequently, and its risks and impacts are not restricted to areas within floodplains or near bodies of water. The objective of this dissertation is to improve our understanding of urban flooding and our capability to predict it through the development
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Shepero, Mahmoud. "Modeling and forecasting the load in the future electricity grid : Spatial electric vehicle load modeling and residential load forecasting." Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359432.

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The energy system is being transitioned to increase sustainability. This transition has been accelerated by the increased awareness about the adverse effects of the greenhouse gas (GHG) emissions into the atmosphere. The transition includes switching to electricity as the energy carrier in some sectors, e.g., transportation, increasing the contribution of renewable energy sources (RES) to the grid, and digitalizing the grid services. Electric vehicles (EVs) are promoted and subsidized in many countries among the sustainability initiatives. Consequently, the global sales of EVs rapidly increase
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Hall, Jeremy T. "Forecasting Marine Corps enlisted attrition through parametric modeling." Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/March/09Mar%5FHall_Jeremy.pdf.

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Thesis (M.S. in )--Naval Postgraduate School, March 2009.<br>Thesis Advisor(s): Buttrey, Samuel E. "March 2009." Description based on title screen as viewed on April 23, 2009. Author(s) subject terms: Forecasting, attrition, Marine Corps NEAS losses, Gompertz Model, survival analysis. Includes bibliographical references (p. 67). Also available in print.
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AJMAL, KHAN, and MAHMOOD HASHMI TAHIR. "Daily Calls Volume Forecasting." Thesis, Högskolan Dalarna, Statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4852.

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A massive amount has been written about forecasting but few articles are written about the development of time series models of call volumes for emergency services. In this study, we use different techniques for forecasting and make the comparison of the techniques for the call volume of the emergency service Rescue 1122 Lahore, Pakistan. For the purpose of this study data is taken from emergency calls of Rescue 1122 from 1st January 2008 to 31 December 2009 and 731 observations are used. Our goal is to develop a simple model that could be used for forecasting the daily call volume. Two differ
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Akter, Lutfa. "Modeling, forecasting and resource allocation in cognitive radio networks." Diss., Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/3892.

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Books on the topic "Forecasting modeling"

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Berk, Kevin. Modeling and Forecasting Electricity Demand. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08669-5.

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Zheng, Xiaolian, and Ben M. Chen. Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5.

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G, Andersen Torben, and National Bureau of Economic Research., eds. Modeling and forecasting realized volatility. National Bureau of Economic Research, 2001.

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Chipika, Jesimen. Macroeconomic modeling and forecasting manual. Macroeconomic and Financial Management Institute of Eastern and Southern Africa, 2012.

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Toshitaka, Sekine. Modeling and forecasting inflation in Japan. International Monetary Fund, Policy Development and Review Department, 2001.

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Callen, Tim. Modeling and forecasting inflation in India. International Monetary Fund, Asia and Pacific Department, 1999.

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G, Siegel Joel, ed. Handbook of financial analysis, forecasting & modeling. Prentice-Hall, 1988.

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Weron, Rafał. Modeling and Forecasting Electricity Loads and Prices. John Wiley & Sons Ltd, 2006. http://dx.doi.org/10.1002/9781118673362.

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Shim, Jae K. Handbook of financial analysis, forecasting, and modeling. 2nd ed. Prentice Hall Press, 2001.

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Soliman, S. A. Electrical load forecasting: Modeling and model construction. Butterworth-Heinemann, 2010.

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Book chapters on the topic "Forecasting modeling"

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Mallios, William S. "Modeling Concepts." In The Analysis of Sports Forecasting. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-6713-1_3.

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Guzik, Marina. "Practical Modeling and Forecasting." In CFO Techniques. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3757-0_35.

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Badiru, Adedeji B., Ibidapo-Obe Oye, and Babatunde J. Ayeni. "Business modeling and forecasting." In Manufacturing and Enterprise. CRC Press, 2018. http://dx.doi.org/10.1201/9780429055928-8.

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Laopodis, Nikiforos T. "Volatility modeling and forecasting." In Financial Economics and Econometrics. Routledge, 2021. http://dx.doi.org/10.4324/9781003205005-15.

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Luo, Yiqi. "Introduction to Ecological Forecasting." In Land Carbon Cycle Modeling, 2nd ed. CRC Press, 2024. http://dx.doi.org/10.1201/9781032711126-37.

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Luo, Yiqi. "Introduction to Ecological Forecasting." In Land Carbon Cycle Modeling. CRC Press, 2022. http://dx.doi.org/10.1201/9780429155659-42.

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Casdagli, Martin. "Nonlinear Forecasting, Chaos and Statistics." In Modeling Complex Phenomena. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9229-3_5.

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Saaty, Thomas L., and Luis G. Vargas. "Modeling Behavior in Competition: Chess." In Prediction, Projection and Forecasting. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7952-0_5.

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Tiwari, Mukesh Kumar, and Sanjeet Kumar. "Reservoir Inflow Forecasting Using Extreme Learning Machines." In Hydrologic Modeling. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5801-1_40.

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Zheng, Xiaolian, and Ben M. Chen. "Market Turning Period Forecasting." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_6.

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Conference papers on the topic "Forecasting modeling"

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Diskin, Yakov, Mitchell Grose, Matthew R. Whiteley, Kevin Jackovitz, and Eric P. Magee. "Weather driven surface level turbulence modeling and forecasting." In Unconventional Imaging, Sensing, and Adaptive Optics 2024, edited by Santasri R. Bose-Pillai, Jean J. Dolne, and Matthew Kalensky. SPIE, 2024. http://dx.doi.org/10.1117/12.3038404.

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K, Vivek, A. R. Deepti, and Farzeen Basith. "Forecasting the Consumer Price Index using SARIMAX Modeling." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00083.

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Magee, Eric, Yakov Diskin, Matthew Whiteley, and Miitchell Grose. "Turbulence Modeling and Forecasting Using Machine Learning and Regional Weather." In Propagation Through and Characterization of Atmospheric and Oceanic Phenomena. Optica Publishing Group, 2024. https://doi.org/10.1364/pcaop.2024.pth4e.1.

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Modeling the relationship between meteorological conditions and optical turbulence is a challenging multifaceted problem. We observed simultaneous weather and turbulence to develop a modeling approach that produces volumetric site-specific models capable of forecasting optical conditions. Full-text article not available; see video presentation
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Gopalakrishnan, Leechita, Julian L. Cardenas Barrera, and N. Sabiyath Fatima. "Short-term Wind Power Ramp Forecasting Using Sequential Approach." In 2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom). IEEE, 2024. https://doi.org/10.1109/mosicom63082.2024.10881012.

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Saltsidou, Eleni, and Maria Drakaki. "Forecasting Tourism Demand in Greece Using Time Series Forecasting." In The 33rd European Modeling & Simulation Symposium. CAL-TEK srl, 2021. http://dx.doi.org/10.46354/i3m.2021.emss.006.

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Ahmad, Farooq, Livio Finos, and Mariangela Guidolin. "Modeling the Future of Hydroelectric Power: A Cross-Country Study." In International conference on Time Series and Forecasting. MDPI, 2024. http://dx.doi.org/10.3390/engproc2024068056.

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Kunitsyn, V., Tatyana Novikova, and A. Shpinev. "FORECASTING THE POPULATION OF VORONEZH CITY IN 2030." In CHALLENGING ISSUES IN SYSTEMS MODELING AND PROCESSES. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2025. https://doi.org/10.58168/cismp2024_57-59.

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This article is dedicated to forecasting the population of Voronezh city in 2030. The main methods of population forecasting are considered, including trend extrapolation methods, analysis of migration flows, and models of socio-economic development. Special attention is paid to accounting for population growth dynamics, migration processes, demographic characteristics, and the impact of socio-economic factors. A comprehensive approach to forecasting the population of Voronezh city in 2030 is proposed, which will allow for the consideration of various factors and contribute to the development
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El Kafazi, Ismail, Rachid Bannari, and Abdellah Abouabdellah. "Modeling and forecasting energy demand." In 2016 International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2016. http://dx.doi.org/10.1109/irsec.2016.7983974.

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Wu, Jennifer, Doina Bein, Jidong Huang, and Sudarshan Kurwadkar. "ETL and ML Forecasting Modeling Process Automation System." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003775.

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Given the importance of online retailers in the market, forecasting sales has become one of the essential market strategic considerations. Modern Machine Learning tools help in forecasting sales for many online retailers. These models need refinement and automatization to increase efficiency and productivity. Suppose an automated function can be applied to capture historical data and execute forecasting models automatically; it will reduce the time and human resources for the company to manage the forecasting system. An automated data processing and forecasting model system offers the marketin
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Wang, Huan, and Yigang He. "Chaotic Multi-step Forecasting Algorism Applied in Short-Time Electric Power Load Forecasting." In 2008 International Symposium on Knowledge Acquisition and Modeling (KAM). IEEE, 2008. http://dx.doi.org/10.1109/kam.2008.40.

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Reports on the topic "Forecasting modeling"

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Andersen, Torben, Tim Bollerslev, Francis Diebold, and Paul Labys. Modeling and Forecasting Realized Volatility. National Bureau of Economic Research, 2001. http://dx.doi.org/10.3386/w8160.

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Libonatti, Luis. MIDAS Modeling for Core Inflation Forecasting. Inter-American Development Bank, 2018. http://dx.doi.org/10.18235/0001250.

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Mccabe, Kirsten, and Rebecca McDonald. Global Disease Modeling & Forecasting Center. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1671063.

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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressi
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Pathak, Parag, and Peng Shi. Demand Modeling, Forecasting, and Counterfactuals, Part I. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w19859.

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Coleman, Andre, Jerry Tagestad, Michael Henry, et al. Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1988243.

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Wharton, S., J. Lundquist, N. Marjanovic, et al. Review of Wind Energy Forecasting Methods for Modeling Ramping Events. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1022139.

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Gafur, Jamil, and Katherine Candice Kempfert. Forecasting Dengue in Brazil with Time Series Modeling in Parallel. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1463575.

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Tuljapurkar, Shripad, and Ryan Edwards. Variance in Death and Its Implications for Modeling and Forecasting Mortality. National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w15288.

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Garen, David. Modeling Techniques for Water Supply Forecasting in the Western United States. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.1322.

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