Relevant bibliographies by topics / Demand Forecasting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Demand Forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Demand Forecasting"

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Jones, Rod. "Forecasting demand." British Journal of Healthcare Management 16, no. 8 (August 2010): 392–93. http://dx.doi.org/10.12968/bjhc.2010.16.8.77654.

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Elkarmi, Fawwaz, and Nazih Abu Shikhah. "Electricity Demand Forecasting." International Journal of Productivity Management and Assessment Technologies 2, no. 1 (January 2014): 1–19. http://dx.doi.org/10.4018/ijpmat.2014010101.

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Forecasting is the backbone of any planning process in all fields of interest. It has a great impact on future decisions. It is also of great importance to the operation and control of business, which is reflected as profits or losses to the entity. This paper aims to provide the planner with sufficient knowledge and background of the different scopes of forecasting methods, in general, and when applied to power system field, in particular. Various load and energy forecasting models, and theoretical techniques are discussed from different perspectives, time frames, and levels. The paper presents the attributes and importance of forecasting through several cases of research conducted by the author for the Jordanian power system. In all cases the methodologies selected cover short, medium and long term forecasting periods and the results are accurate.
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Greenidge, Kevin. "Forecasting tourism demand." Annals of Tourism Research 28, no. 1 (January 2001): 98–112. http://dx.doi.org/10.1016/s0160-7383(00)00010-4.

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O, Nepochatenko Olena. "Forecasting Investment Demand of Ukrainian Agrarian Enterprises." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 359–69. http://dx.doi.org/10.5373/jardcs/v12sp7/20202117.

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Kandananond, Karin. "Applying Kalman Filter for Correlated Demand Forecasting." Applied Mechanics and Materials 619 (August 2014): 381–84. http://dx.doi.org/10.4028/www.scientific.net/amm.619.381.

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Product demands are known to be serially correlated. In this research, a first order autoregressive model, AR (1), is utilized to simulate product demand processes whose behavior are stationary. Since demand forecasting is important to the efficiency improvement of product supply chain system, different forecasting techniques are utilized to predict product demand. In this research, Kalman filter is deployed to forecast demand simulated by AR (1) model. Product demands are simulated at the different degrees of autoregressive coefficients. After the application of Kalman filter to the designated data, the forecasting errors are calculated and the results indicate that Kalman filter is an efficient technique to predict demands in the future.
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Bernard Trustrum, Leslie, F. Robert Blore, and William James Paskins. "Using Demand Forecasting Models." Marketing Intelligence & Planning 5, no. 3 (March 1987): 5–15. http://dx.doi.org/10.1108/eb045750.

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Martin, Christine A., and Stephen F. Witt. "Tourism demand forecasting models." Tourism Management 8, no. 3 (September 1987): 233–46. http://dx.doi.org/10.1016/0261-5177(87)90055-0.

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Chambers, Marcus J. "Forecasting with demand systems." Journal of Econometrics 44, no. 3 (June 1990): 363–76. http://dx.doi.org/10.1016/0304-4076(90)90064-z.

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Wong, James M. W., Albert P. C. Chan, and Y. H. Chiang. "Construction manpower demand forecasting." Engineering, Construction and Architectural Management 18, no. 1 (January 11, 2011): 7–29. http://dx.doi.org/10.1108/09699981111098667.

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Guinel, Ipek. "Forecasting system energy demand." Journal of Forecasting 6, no. 2 (1987): 137–56. http://dx.doi.org/10.1002/for.3980060207.

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Dissertations / Theses on the topic "Demand Forecasting"

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Martin, C. A. "International tourism demand forecasting." Thesis, University of Bradford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379816.

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Syntetos, Argyrios. "Forecasting of intermittent demand." Thesis, Online version, 2001. http://bibpurl.oclc.org/web/26215.

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Gato, Shirley, and s3024038@rmit edu au. "Forecasting Urban Residential Water Demand." RMIT University. Civil, Environmental and Chemical Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070202.113452.

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The city of Melbourne in Victoria, Australia has been recognised as having high quality drinking water, but like other urban cities in the world, its growing population means increasing water demand. Melbourne is also already on its eight year of dry climatic conditions and is currently experiencing a drought that forced water authorities to impose water restrictions after 20 years of unrestricted supply. The current drought, dwindling supplies and possible impact of climate change highlight the importance of making better use of this precious resource. The Water Resources Strategy has been developed for Melbourne, which serve as the basis for the Victorian Government to set per capita consumption reduction targets of 15%, 25% and 30% by 2010, 2015 and 2020 respectively. The strategy was developed to ensure a continuation of a safe, reliable and cost effective water supply that is environmentally sustainable in the long term. This is in recognition that population growth and water consumption will eventually require additional supplies of water (Water Resources Strategy Committee for the Melbourne Area 2002). One of the key findings of the National Land and Water Resources Audit's Australian Water Resources Assessment 2000 is the lack of detailed knowledge about the end use (Australian Water Association 2001). The
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Rostami, Tabar Bahman. "ARIMA demand forecasting by aggregation." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00980614.

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Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce demand uncertainty and consequently improve the forecasting (and inventory control) performance. An intuitively appealing such approach that is known to be effective is demand aggregation. One approach is to aggregate demand in lower-frequency 'time buckets'. Such an approach is often referred to, in the academic literature, as temporal aggregation. Another approach discussed in the literature is that associated with cross-sectional aggregation, which involves aggregating different time series to obtain higher level forecasts.This research discusses whether it is appropriate to use the original (not aggregated) data to generate a forecast or one should rather aggregate data first and then generate a forecast. This Ph.D. thesis reveals the conditions under which each approach leads to a superior performance as judged based on forecast accuracy. Throughout this work, it is assumed that the underlying structure of the demand time series follows an AutoRegressive Integrated Moving Average (ARIMA) process.In the first part of our1 research, the effect of temporal aggregation on demand forecasting is analysed. It is assumed that the non-aggregate demand follows an autoregressive moving average process of order one, ARMA(1,1). Additionally, the associated special cases of a first-order autoregressive process, AR(1) and a moving average process of order one, MA(1) are also considered, and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregate and the non-aggregate demand in order to contrast the relevant forecasting performances. The theoretical analysis is validated by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant value used for SES and the process parameters.In the second part of our research, the effect of cross-sectional aggregation on demand forecasting is evaluated. More specifically, the relative effectiveness of top-down (TD) and bottom-up (BU) approaches are compared for forecasting the aggregate and sub-aggregate demands. It is assumed that that the sub-aggregate demand follows either a ARMA(1,1) or a non-stationary Integrated Moving Average process of order one, IMA(1,1) and a SES procedure is used to extrapolate future requirements. Such demand processes are often encountered in practice and, as discussed above, SES is one of the standard estimators used in industry (in addition to being the optimal estimator for an IMA(1) process). Theoretical Mean Squared Errors are derived for the BU and TD approach in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation at both the aggregate and sub-aggregate levels in addition to empirically validating our findings on a real dataset from a European superstore. The results show that the superiority of each approach is a function of the series autocorrelation, the cross-correlation between series and the comparison level.Finally, for both parts of the research, valuable insights are offered to practitioners and an agenda for further research in this area is provided.
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GOMES, RENATA MIRANDA. "BIAS DETECTION IN DEMAND FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18477@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Essa dissertação teve como objetivo propor dois novos métodos para detecção de viés na previsão de demanda. Os métodos consistem numa adaptação de duas técnicas de controle estatístico de processos, o gráfico de controle de EWMA e o algoritmo CUSUM, ao contexto de detecção de viés na previsão de demanda. O desempenho dos métodos foi analisado por simulação, para diversos casos de mudança na inclinação (tendência) da série de dados (mudança de modelo constante para modelo com tendência; alteração na tendência de série crescente; estabilização de série crescente em um patamar constante), e com diferentes parâmetros para os métodos. O estudo limitou-se a séries sem sazonalidade e aos métodos de previsão de amortecimento exponencial simples e de Holt. Os resultados mostraram a grande superioridade do gráfico de EWMA proposto e apontam questões para pesquisas futuras.
The purpose of this dissertation is to propose two new methods for detection of biases in demand forecasting. These methods are adaptations of two statistical process control techniques, the EWMA control chart and the CUSUM control chart (or CUSUM algorithm), to the context of the detection of biases in demand forecasting. The performance of the proposed methods was analyzed by simulation, for several magnitudes of changes in the trend of the series (change from a level series to a series with a trend, changes in the trend parameter, and stabilization of a series with a trend in a constant average level) and with different parameters for all methods. The study was limited to non-seasonal models and to the methods of simple exponential smoothing and Holt’s Exponential Smoothing. The results have shown the superiority of the EWMA method proposed and indicate issues for future research.
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Holbrook, Blair Sato. "Point-of-sale demand forecasting." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104397.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 38).
Nike Always Available (AA) is a significant global business unit within Nike that allows retail customers to purchase athletic essentials at weekly replenishment intervals and 95% availability. However, demand fluctuations and current forecasting processes have resulted in frequent stock-outs and inventory surpluses, which in turn affect revenue, profitability, and brand trust. Potential root causes for demand fluctuations have included: -- Erratic customer behavior, including unplanned promotional events, allocation of open-to- buy dollars for futures (i.e., contract) versus replenishment (i.e., AA), and product inventory loading to protect from anticipated stock-outs; -- Lack of incentives and accountability to encourage accurate forecasting by customers. Current forecasting processes, which utilize historical sell-in data (i.e., product sold to retail customers) were found to be significantly inaccurate - 100% MAPE. The goal of this project was to develop a more accurate forecast based on historical sell-through data (i.e., product sold to consumers), which were recently made available. Forecast error was drastically reduced using the new forecasting method - 35% MAPE. A pilot was initiated with a major retail customer in order to test the new forecast model and determine the effects of a more transparent ordering partnership. The pilot is ongoing at the time of thesis completion.
by Blair Sato Holbrook.
M.B.A.
S.M. in Engineering Systems
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Al-Madfai, Hasan. "Weather corrected electricity demand forecasting." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/weather-corrected-electricity-demand-forecasting(2e066cc4-58b1-4694-9937-ee8f57fbed02).html.

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Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to provide short-term load forecasts. Two novel time series modelling approaches were introduced and developed. Profiles ARIMA (PARIMA) and the Variability Decomposition Method (VDM). PARIMA is a univariate modelling approach that is based on the hierarchical modelling of the different components of the electricity demand series as deterministic profiles, and modelling the remainder stochastic component as ARIMA, serving as a simple yet versatile signal extraction procedure and as a powerful prewhitening technique. The VDM is a robust transfer function modelling approach that is based on decomposing the variability in time series data to that of inherent and external. It focuses the transfer function model building on explaining the external variability of the data and produces models with parameters that are pertinent to the components of the series. Several candidate input variables for the VDM models for electricity demand were investigated, and a novel collective measure of temperature the Fair Temperature Value (FTV) was introduced. The FTV takes into account the changes in variance of the daily maximum and minimum temperatures with time, making it a more suitable explanatory variable for the VDM model. The novel PARIMA and VDM approaches were used to model the quarterly, monthly, weekly, and daily demand series. Both approaches succeeded where existing approaches were unsuccessful and, where comparisons are possible, produced models that were superior in performance. The VDM model with the FTV as its explanatory variable was the best performing model in the analysis and was used for weather correction. Here, weather corrected forecasts were produced using the weather sensitive components of the PARIMA models and the FTV transfer function component of the VDM model.
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Fernandes, Filho Roberto Braga. "Integração de modelos de previsão de demanda qualitativos e quantitativos e comparação com seus desempenhos individuais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/117896.

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Um bom sistema de previsão de demanda é um dos passos para o sucesso de uma empresa. Previsões com baixos erros permitem a manutenção de um estoque reduzido, uma ocupação de fábrica e uma gestão financeira mais eficiente, em conjunto com outros benefícios trazidos por um sistema confiável. Há diversas formas de realizar uma previsão, mas há anos a que vem sendo considerada mais promissora é a que integra métodos quantitativos e qualitativos. Ambos os métodos possuem vantagens exclusivas, o que torna a integração particularmente interessante. Este trabalho visa o desenvolvimento e teste de um sistema de previsão em uma empresa de grande porte, a fim de disponibilizar uma forma confiável de integração de métodos. Busca ainda validar o auxilio de especialistas nos ajustes de previsão de forma que os problemas provenientes do julgamento humano possam ser evitados. Uma comparação entre as várias previsões realizadas é apresentada, de forma que o leitor possa interpretá-las e julgar quais possam ser as mais adequadas à situação em que se encontra.
A good forecasting system is one of the steps to the success of a company. Forecasts with small errors enable the maintenance of a reduced inventory, a more efficient factory occupation and financial management, together with other benefits provided by a reliable system. There are several ways to make a forecast, but for years the quantitative and qualitative methods integrated has been considered more promising. Both methods have unique advantages which makes it particularly interesting integration. This paper aims to develop and test a forecasting system in a large company in order to provide a reliable form of integration methods. It also seeks to validate the aid of experts in the predictive adjustments so that problems derived from the human judgment can be avoided. A comparison of the various forecasts made is provided in a way that the reader can interpret them and judge which may be the most appropriate to the situation you are in.
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Ho, Kien K. (Kine Kit). "Demand forecasting for aircraft engine aftermarket." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43833.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.
Includes bibliographical references.
In 2006, Pratt and Whitney launched the Global Material Solutions business model aiming to supply spare parts to non-OEM engines with minimum 95% on-time delivery and fill-rate. Lacking essential technical knowledge of the target engines, predictability and associated confidence of the parts demands are very limited. This thesis focuses on exploring alternative and innovative approaches to providing more accurate demand forecasts based on limited information. Approaches including application of fundamental sampling theorems, random walk simulations based on Markov Chain simplification, and sensitivity analysis on incremental scrap rates were introduced. A software tool, based on the sensitivity analysis was introduced for all gas path parts. The methodology could potentially be applicable to industries other than Aerospace.
by Kien K. Ho.
S.M.
M.B.A.
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Obbiso, Pietro. "Forecasting intermittent demand: a comparative approach." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24792/.

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In this thesis, a comparative approach between forecasting intermittent demand by using machine learning and by using statistical models is carried out. Models implementations are done with the support of different Python libraries aimed at discovering which model would provide better results. For what concerns the data, an electricity demand dataset is used for building the models, where their generated predictions are compared with the real ones. Moreover, performances are investigated against specifically selected scenarios, where different forecast horizons and different times of the day are considered. It gave us the possibility of analysing how these models would perform over distinct settings, including the ones during an anomalous period. The final results showed the KNeighbors Regressor being the best model, especially in scenarios that consider moments in time of very low demand in a normal week, with an accuracy value of 93%. However, despite being the best result, it is not the most intriguing to consider, as instead discovering how some forecasters perform surprisingly well in scenarios where the anomaly is present is the main interest.
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Books on the topic "Demand Forecasting"

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Baker, Bill. Demand forecasting methodology. London: UK Water Industry Research Limited, 1995.

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Chase, Charles, ed. Demand-Driven Forecasting. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119203612.

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Chase, Charles W. Demand-Driven Forecasting. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118691861.

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Coulbeck, B. Water demand forecasting. Leicester: Leicester Polytechnic, 1985.

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Chase, Charles. Demand-Driven Forecasting. New York: John Wiley & Sons, Ltd., 2009.

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Billings, R. Bruce. Forecasting urban water demand. 2nd ed. Denver, Colo: American Water Works Association, Science and Technology, 2007.

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Hong, Wei-Chiang. Intelligent Energy Demand Forecasting. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4968-2.

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Vaughan, Jones Clive, ed. Forecasting urban water demand. Denver, Colo: American Water Works Association, 1996.

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Hong, Wei-Chiang. Intelligent Energy Demand Forecasting. London: Springer London, 2013.

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Demand-driven forecasting: A structured approach to forecasting. Hoboken, N.J: John Wiley & Sons, 2009.

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Book chapters on the topic "Demand Forecasting"

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Zhou, Dadi, Shixian Gao, Edgard Gnansounou, and Jun Dong. "Demand Forecasting." In Integrated Assessment of Sustainable Energy Systems in China The China Energy Technology Program, 43–98. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0153-3_4.

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Sene, Kevin. "Demand Forecasting." In Hydrometeorology, 183–207. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23546-2_6.

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Mauergauz, Yuri. "Demand Forecasting." In Advanced Planning and Scheduling in Manufacturing and Supply Chains, 199–214. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27523-9_6.

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Sene, Kevin. "Demand Forecasting." In Hydrometeorology, 141–69. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3403-8_5.

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Ivanov, Dmitry, Alexander Tsipoulanidis, and Jörn Schönberger. "Demand Forecasting." In Springer Texts in Business and Economics, 301–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24217-0_11.

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Das, Satya P., and J. K. Goyal. "Demand Forecasting." In Economics for Managers, 171–94. London: Routledge India, 2024. http://dx.doi.org/10.4324/9781003452195-6.

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Ivanov, Dmitry, Alexander Tsipoulanidis, and Jörn Schönberger. "Demand Forecasting." In Springer Texts in Business and Economics, 341–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72331-6_11.

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Brown, Mike. "Demand Forecasting." In Strategic Airport Planning, 26–57. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003173267-3.

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Ivanov, Dmitry, Alexander Tsipoulanidis, and Jörn Schönberger. "Demand Forecasting." In Springer Texts in Business and Economics, 319–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94313-8_11.

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Doganis, Rigas. "Forecasting demand." In Flying Off Course, 188–212. 5th Edition. | New York : Routledge, 2019. | Revised edition of the author’s Flying off course, 2010.: Routledge, 2019. http://dx.doi.org/10.4324/9781315402987-10.

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Conference papers on the topic "Demand Forecasting"

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Sinn, Mathieu. "Energy demand forecasting." In e-Energy '14: The Fifth International Conference on Future Energy Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2602044.2602086.

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Tang, Zhongjun, and Jing Xiao. "Tools for a New Demand Forecasting Paradigm "Individual Demand Forecasting'." In 2009 International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2009. http://dx.doi.org/10.1109/bife.2009.76.

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Tang, Zhongjun. "Mechanism for a New Demand Forecasting Paradigm 'Individual Demand Forecasting'." In 2010 3rd International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2010. http://dx.doi.org/10.1109/bife.2010.33.

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Garrow, Laurie A., Mohammad Ilbeigi, and Ziran Chen. "Forecasting Demand for On Demand Mobility." In 17th AIAA Aviation Technology, Integration, and Operations Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-3280.

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Meng, Xiangang, Hongsen Yan, and Yufang Wang. "Product Demand Forecasting in Knowledgeable Manufacturing Based on Demand Forecasting Net." In 2009 First International Workshop on Database Technology and Applications, DBTA. IEEE, 2009. http://dx.doi.org/10.1109/dbta.2009.120.

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Chen, Hu, and Hua He. "Reverse Logistics Demand Forecasting under Demand Uncertainty." In International Conference of Logistics Engineering and Management (ICLEM) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41139(387)49.

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Wheeler, David R., and Charles Shelley. "Forecasting Artificial Intelligence Demand." In 1986 Technical Symposium Southeast, edited by John F. Gilmore. SPIE, 1986. http://dx.doi.org/10.1117/12.964184.

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Veit, Andreas, Christoph Goebel, Rohit Tidke, Christoph Doblander, and Hans-Arno Jacobsen. "Household electricity demand forecasting." In e-Energy '14: The Fifth International Conference on Future Energy Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2602044.2602082.

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Filippov, Sergey P., Natalia A. Grygorieva, and Elena M. Makarova. "Energy Demand Forecasting System." In 2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2018. http://dx.doi.org/10.1109/fareastcon.2018.8602786.

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Zhongjun Tang and Xiaohong Chen. "A new demand forecasting paradigm ‘customer-centric individual demand forecasting’." In 2007 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2007. http://dx.doi.org/10.1109/ieem.2007.4419370.

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Reports on the topic "Demand Forecasting"

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Goodwill, Jay. Forecasting Paratransit Services Demand – Review and Recommendations. Tampa, FL: University of South Florida, June 2013. http://dx.doi.org/10.5038/cutr-nctr-rr-2011-06.

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Pathak, Parag, and Peng Shi. Demand Modeling, Forecasting, and Counterfactuals, Part I. Cambridge, MA: National Bureau of Economic Research, January 2014. http://dx.doi.org/10.3386/w19859.

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Verma, Rajat, Hao Luo, Saloni Deodhar, Eunhan Ka, Ricardo Chahine, Pallavi Natu, Harshit Malhotra, et al. Forecasting Shifts in Hoosiers’ Travel Demand and Behavior. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317685.

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The changing landscape of transportation technology and traveler behavior, accelerated by recent events like COVID-19, has led to significant shifts in travel demand and vehicle miles traveled in Indiana. This study seeks to understand the long-term implications of these changes and their potential impact on passenger, freight, and micro-mobility movements across the state. To achieve this objective, this project focused on forecasting future transportation demand conditions and carrying out long-range scenario planning by accomplishing four tasks: forecasting travel demand shifts based on location-based data, evaluating medium-term inter- and intra-urban transportation demand shifts, forecasting county-level industry shifts using scenario-based growth models, and providing recommendations and guidance to the Indiana Department of Transportation (INDOT) based on the study results. Results offer improved planning for infrastructure investments and operations, the incorporation of emerging technologies into transportation planning processes, and an enhanced understanding of passenger and freight movements at the statewide and regional levels. Deliverables from this study include valuable tools and models that can help INDOT navigate potential transportation system changes and accommodate the evolving needs of the future.
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Kiflu, Mordocai, and Carlos Lopez. Demand Forecasting: DLA'S Aviation Supply Chain High Value Products. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada626751.

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Langstroth, IV, and Theodore A. Forecasting Demand for KC-135 Sorties: Deploy to Dwell Impacts. Fort Belvoir, VA: Defense Technical Information Center, June 2013. http://dx.doi.org/10.21236/ada587364.

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Orchowsky, Stan, Ronald Kirchoff, Janet Rider, and Dale Kem. A Study of Demand Forecasting in the Defense Logistics Agency. Fort Belvoir, VA: Defense Technical Information Center, February 1986. http://dx.doi.org/10.21236/ada169637.

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Thumukunta, Manogna. A Comparative Study on Various Demand Forecasting Techniques and Performance Metrics. Ames (Iowa): Iowa State University, December 2023. http://dx.doi.org/10.31274/cc-20240624-22.

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Mikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, Fakhri Hasanov, Saleh T. AlTurki, and Rey B. Arnaiz. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp19.

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The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions.
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Mikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, and Fakhri Hasanov. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp22.

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The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions.
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Hersey, J. M., James M. Rowlett, and Shannon P. Thompson. Forecasting the Demand of the F414-GE-400 Engine at NAS Lemoore. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada493918.

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