Relevant bibliographies by topics / Demand forecasting methods

Contents

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

Academic literature on the topic 'Demand forecasting methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Demand forecasting methods"

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Natarajan, Manikandan. "Artificial Intelligence Methods for Demand Forecasting." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 442–46. http://dx.doi.org/10.21275/sr241005193553.

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Wang, Yawen. "Overview of Logistics Demand Forecasting Methods." Frontiers in Business, Economics and Management 9, no. 2 (2023): 251–55. http://dx.doi.org/10.54097/fbem.v9i2.9293.

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Accurate forecasting of logistics demand is of great theoretical significance and practical application value for the formulation of national policies and the satisfaction of actual demand in the logistics industry. From the modeling form, the existing logistics demand forecasting methods are divided into four categories: single traditional forecasting method, single intelligent forecasting method, combined forecasting method and mixed forecasting method. Among them, single traditional forecasting methods mainly include simple time series method, regression analysis, mathematical and statistic
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Strotsen, L. "Qualitative methods of forecasting demand." Galic'kij ekonomičnij visnik 54, no. 1 (2018): 113–18. http://dx.doi.org/10.33108/galicianvisnyk_tntu2018.01.113.

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Kalev, Krasimir. "Application of statistical methods in forecasting for spare elements demand." Journal scientific and applied research 2, no. 1 (2012): 34–38. http://dx.doi.org/10.46687/jsar.v2i1.40.

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The problem of predicting the demand for spare elements is extremely important for continuous operation of machines. It is necessary to know when and how much to order. To calculate and ensure the availability of spare elements, appropriate mathematical models should be applied. The statistical method for predicting spare elements demand is considered in this paper. The study shows how to use a demand forecasting technique for determining the expected number of spare elements. Some of the results are given by engineering software.
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Bar-On, Raphael R. "Forecasting Tourism Demand: Methods and Strategies." Annals of Tourism Research 30, no. 3 (2003): 754–56. http://dx.doi.org/10.1016/s0160-7383(03)00051-3.

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Irawan, Suhendi, Hendri Wijaya, Annisa Kartinawati, and Sazli T Risyahadi. "Production Planning to Meet Maximum Demand with Forecasting and Aggregation Methods." E3S Web of Conferences 454 (2023): 03018. http://dx.doi.org/10.1051/e3sconf/202345403018.

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Production carried out by small industries still needs to be improved and classified as insufficient compared to the consumer demand. The company’s production traditionally relies on sunlight in the drying process. This is the main factor in the limited production process that can be carried out. This study aims to overcome the maximum demand, which is still an obstacle for the industry to meet their demands. To overcome this, demand forecasting is carried out to determine the demand forecast in advance, and production adjustments are made to meet maximum demand with aggregate planning. Data c
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Deakshinamurthyt. "DEMAND FORECASTING FOR CEMENT IN INDIA 2030." International Journal of Marketing & Financial Management Volume 5, Issue 8, Aug-2017 (2017): pp 09–13. https://doi.org/10.5281/zenodo.888259.

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The paper estimates the demand forecasting for cement in India for 2030 based on exponential demand forecasting, linear forecasting and polynomial method. Cement being an important raw material for construction and infrastructure developments it supports in constructing a better nation. Three different methods for forecasting of cement production is done. The exponential method is suitable for high economic activity in India and polynomial (degree two) for average level of economic activity and linear forecasting for a low level of economic activity in the country.
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Andrea, KOLKOVÁ. "Comparison of Demand Forecasting Methods for Global and Local Demand: The Case of Classic Literature Demand Forecasting." ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH 59, no. 2/2025 (2025): 292–307. https://doi.org/10.24818/18423264/59.2.25.18.

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Chu, Fong-Lin. "Forecasting tourism demand with ARMA-based methods." Tourism Management 30, no. 5 (2009): 740–51. http://dx.doi.org/10.1016/j.tourman.2008.10.016.

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EMEÇ, Şeyma, and Duygu TEKİN. "Housing Demand Forecasting with Machine Learning Methods." Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 15, Special Issue 1 (2022): 36–52. http://dx.doi.org/10.18185/erzifbed.1199535.

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Housing is a place where sustainable urban spaces are produced and where people's physical, cultural, environmental, economic, social and psychological needs are evaluated together with their surroundings, rather than just a building where the need for shelter is met. With the acceleration of urbanization, new needs arise, and the first of these is the need for housing. The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing. The need for adequate and accessible housing comes to the forefront in our country as well as in
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Dissertations / Theses on the topic "Demand forecasting methods"

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Sani, Babangida. "Periodic inventory control systems and demand forecasting methods for low demand items." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309040.

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Jeon, Gyoo Jeong. "Innovative methods for long-term mineral forecasting." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184653.

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This study presents improved methods for long-term forecasting of mineral demands. Intensity of use, both in its simple, original form and as described by richer economic relations is one such method, particularly when intensity of use is estimated using rigorous statistical methods. Additionally, this dissertation explores the implications of the learning curve for long term forecasting of mineral demands. This study begins by considering the empirical evidence which applies when a learning curve is present. Then, if a learning pattern is present, the learning model is used to examine an econ
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INCREMONA, ALESSANDRO. "Machine Learning methods for long and short term energy demand forecasting." Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1436355.

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The thesis addresses the problems of long- and short- term electric load demand forecasting by using a mixed approach consisting of statistics and machine learning algorithms. The modelling of the multi-seasonal component of the Italian electric load is investigated by spectral analysis combined with machine learning. In particular, a frequency-domain version of the LASSO is developed in order to enforce sparsity in the parameter and efficiently obtain the main harmonics of the multi-seasonal term. The corresponding model yields one-year ahead forecasts whose Mean Absolute Percentage Error (MA
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Silva, Jesús, Naveda Alexa Senior, Guliany Jesús García, Núẽz William Niebles, and Palma Hugo Hernández. "Forecasting Electric Load Demand through Advanced Statistical Techniques." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652142.

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Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.
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Davydenko, Andrey. "Integration of judgmental and statistical approaches for demand forecasting : models and methods." Thesis, Lancaster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655734.

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The need for the composite use of judgmental and statistical approaches in forecasting is caused by the fact that each of these approaches itself cannot ensure the desired quality of forecasts. The topic of integrating forecasting methods has been long addressed in literature. However, due to the specific nature of demand data existing solutions in this area often cannot be efficiently applied in demand forecasting. The aim of the research is to develop efficient models and methods which would better correspond to realistic problem definitions in the context of demand forecasting. The first qu
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Larsson, Felix, and Robin Linna. "An Analysis of Passenger Demand Forecast Evaluation Methods." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139942.

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In the field of aviation forecasting is used, among other things, to determine the number of passengers to expect for each flight. This is beneficial in the practice of revenue management, as the forecast is used as a base when setting the price for each flight. In this study, a forecast evaluation has been done on seven different routes with a total of 61 different flights, using four different methods. These are: Mean Absolute Scaled Error (MASE), Mean Absolute Percentage Error (MAPE), Tracking Signal, and a goodness of fit test to determine if the forecast errors are normally distributed. T
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Wong, Ho-ting, and 黃浩霆. "Biometeorological modelling and forecasting of ambulance demand for Hong Kong: a spatio-temporal approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B4775297X.

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The demand for emergency ambulance services in Hong Kong is on the rise. Issues such as climate change, ageing population, constrained space, and limited resource capacity mean that the present way of meeting service demand by injecting more resources will reach its limit in the near future and unlikely to be sustainable. There is an urgent need to develop a more realistic forecast model to account for the anticipated demand for emergency ambulance services to enable better strategic planning of resources and more effective logistic arrangement. In this connection, the research objecti
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Velasco, Leandro Henz. "Previsão de demanda de acessos móveis no sistema de telefonia brasileiro." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/13454.

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No presente trabalho são aplicadas ferramentas estatísticas quantitativas clássicas no setor de telefonia móvel brasileiro a fim de comparar os seus resultados. Uma metodologia é proposta para a aplicação destas ferramentas de um modo prático em uma de uma operadora de telefonia celular brasileira. Após são aplicados os métodos de previsão às séries históricas de demanda de acessos da telefonia móvel brasileira, estratificadas de acordo com as tecnologias utilizadas (TDMA, CDMA e GSM), no período de agosto de 2002 a julho de 2007. De acordo com o desempenho, são obtidos os modelos de cada méto
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Lima, Diego Duarte. "A study of demand forecasting cashew trade in Cearà through multivariate time series." Universidade Federal do CearÃ, 2013. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12185.

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nÃo hÃ<br>The application of time series in varius areas such as engineering, logistics, operations research and economics, aims to provide the knowledge of the dependency between observations, trends, seasonality and forecasts. Considering the lack of effective supporting methods od logistics planning in the area of foreign trade, the multivariate models habe been presented and used in this work, in the area of time series: vector autoregression (VAR), vector autoregression moving-average (VARMA) and state-space integral equation (SS). These models were used for the analysis of demand foreca
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Winn, David. "An analysis of neural networks and time series techniques for demand forecasting." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004362.

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This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasti
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Books on the topic "Demand forecasting methods"

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

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C, Frechtling Douglas, ed. Forecasting tourism demand: Methods and strategies. Butterworth-Heinemann, 2001.

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Yetley, Mervin J. Projecting food demand: A comparison of two methods. U.S. Dept. of Agriculture, Economic Research Service, International Economics Division, 1986.

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Yetley, Mervin J. Projecting food demand: A comparison of two methods. U.S. Dept. of Agriculture, Economic Research Service, International Economics Division, 1986.

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Yetley, Mervin J. Projecting food demand: A comparison of two methods. U.S. Dept. of Agriculture, Economic Research Service, International Economics Division, 1986.

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Coe, Manchester Alden. Data for food demand analysis: Availability, characteristics, options. U.S. Dept. of Agriculture, Economic Research Service, 1990.

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Mijumbi, Peter B. Estimation of regional staple food demand elasticities using 1993-4 Uganda national survey data. Economic Policy Research Center, 1999.

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Bahreinian, Aniss. Transportation fuel price and demand forecasts: Inputs and methods for the 2009 integrated energy policy report : draft staff report. California Energy Commission, 2009.

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Ediev, Dalhat. Fundamentals of demographic analysis with elements of mathematical demography. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2106167.

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The textbook is devoted to the presentation of methods of demographic analysis and demographic models that are in demand in the practice of analyzing the structure, fertility, mortality, migration and reproduction of the population. It was the result of many years of teaching subjects in mathematical demography, demographic analysis, mortality analysis and demographic forecasting, as well as the practical and research experience of the author. Meets the requirements of the latest generation of federal state educational standards for higher education. For students and postgraduates studying dem
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Food and Agriculture Organization of the United Nations., ed. Guidelines on employment: Supplement to the Programme for the world census of agriculture 2000. FAO, 1997.

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Book chapters on the topic "Demand forecasting methods"

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Jaiwant, Sunanda Vincent, and Joseph Varghese Kureethara. "Demand Forecasting Methods." In Artificial Intelligence in Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003399292-20.

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Takano, Willian Y., and Eduardo N. Asada. "Developing Energy Demand Forecasting Methods." In Handbook of Smart Energy Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72322-4_47-1.

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Takano, Willian Y., and Eduardo N. Asada. "Developing Energy Demand Forecasting Methods." In Handbook of Smart Energy Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-97940-9_47.

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Hong, Wei-Chiang. "Data Pre-processing Methods." In Hybrid Intelligent Technologies in Energy Demand Forecasting. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36529-5_3.

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Haben, Stephen, Marcus Voss, and William Holderbaum. "Probabilistic Forecast Methods." In Core Concepts and Methods in Load Forecasting. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27852-5_11.

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AbstractThe previous two chapters were concerned with point forecasts which only produce a single estimate for each time step in the forecast horizon, i.e. one value $$L_t$$ L t for each of the time periods $$t=N+1, N+2, \ldots , N+k$$ t = N + 1 , N + 2 , … , N + k (assuming a forecast horizon of length k steps ahead starting at forecast origin N). Point estimates are limited in their description of the future demand, especially when the underlying data has a large degree of uncertainty. A more detailed picture of the possible values of the demand can be produced by estimating the distribution
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Yao, Liya, and Lishan Sun. "Practical Methods in Traffic Demand Forecasting Model." In Atlantis Computational Intelligence Systems. Atlantis Press, 2012. http://dx.doi.org/10.2991/978-94-91216-80-0_15.

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Haben, Stephen, Marcus Voss, and William Holderbaum. "Case Study: Low Voltage Demand Forecasts." In Core Concepts and Methods in Load Forecasting. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27852-5_14.

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AbstractThis chapter demonstrates the practical implementation of short term (day-ahead) forecasts for the application of residential low voltage networks. It is split into two main parts: An in-depth examination of a short term forecasting case study of residential low voltage networks (Sect. 14.2); and a example python code demonstrating how to implement some of the methods and techniques in practice (Sect. 14.3).
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Wang, Wei, Jin Zhu, and Pingxin Wang. "Data Analytics Methods in Human Resource Demand Forecasting." In Data Mining and Big Data. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0837-6_1.

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Öztayşi, Başar, and Eda Bolturk. "Fuzzy Methods for Demand Forecasting in Supply Chain Management." In Supply Chain Management Under Fuzziness. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53939-8_11.

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Medina, Héctor, Mario Peña, Lorena Siguenza-Guzman, and Rodrigo Guamán. "Demand Forecasting for Textile Products Using Machine Learning Methods." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03884-6_23.

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Conference papers on the topic "Demand forecasting methods"

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Liu, Ping. "Demand Forecasting: Cross-Functional, Cross-Disciplinary Analytics." In Vertical Flight Society 73rd Annual Forum & Technology Display. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12219.

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Accurate material demand forecasting can lead to significant cost savings, greater competitiveness and improved customer satisfaction. However, more often than not, demand forecasting as a business function is carried out poorly, with forecast accuracy often not significantly better than the naïve forecast. To appropriately address these concerns and satisfy overall business objectives, it's increasingly important to have a holistic strategy to improve demand forecast accuracy through a well thought-out enterprise data strategy, applications of advanced forecasting methods as well as synchroni
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Prastuti, Mike, I. Nyoman Pujawan, Erwin Widodo, and Heri Kuswanto. "Demand Forecasting Model for Cement Using Spatial Time Series Methods." In 2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC). IEEE, 2024. https://doi.org/10.1109/temscon-aspac62480.2024.11025002.

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Zhou, Lin, Kun Wang, and Weiwei Zhang. "ICT industry development and electricity demand forecasting based on machine learning methods." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051349.

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Alvarez Orbe, Washington S., Johanna P. Orellana Iñiguez, Cynthia E. Alvarez Orbe, Guilherme Souto Chagas, and Waldyr L. R. Gallo. "Short-term Load Demand Forecasting with Machine Learning Methods: A Brazilian Case Study." In 2024 IEEE PES Generation, Transmission and Distribution Latin America Conference and Industrial Exposition (GTDLA). IEEE, 2024. https://doi.org/10.1109/gtdla61236.2024.10913792.

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Sangngamseung, Narongsak, Anawin Sanarak, Sorrawit Khunwiset, Priyaporn Ratsame, and Pakpoom Chansri. "Comparison Forecasting Methods for Electrical Energy Demand: In Case Study Luggage Manufacturing Industry." In 2025 13th International Electrical Engineering Congress (iEECON). IEEE, 2025. https://doi.org/10.1109/ieecon64081.2025.10987771.

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Leokhin, Yuri, Timur Fatkhulin, Alexey Zanegin, and Aziza Rakhmatova. "Researching the Efficiency of Machine Learning Methods Used in Forecasting Demand for Certain Types of Goods." In 2025 Systems of Signals Generating and Processing in the Field of on Board Communications. IEEE, 2025. https://doi.org/10.1109/ieeeconf64229.2025.10948113.

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Sancar, Semanur, Meryem Açelya Kasapoğlu, Ayşe Kübra Tatar, and Ozan Erdinç. "Harnessing Diverse Data for Hourly Electricity Demand Forecasting in Türkiye: Comparative Analysis of AI-Based Methods." In 2025 7th Global Power, Energy and Communication Conference (GPECOM). IEEE, 2025. https://doi.org/10.1109/gpecom65896.2025.11061840.

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Zhu, S. Z., S. J. Jedidia, and S. Z. Djokic. "Forecasting Aggregate GB Electricity Demand with Hybrid CNN and BiLSTM Methods in Stack and Parallel Structures and Temperature Dependency." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10689059.

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Zhan, Zhigang. "Research on Power Demand Forecasting Methods." In 2nd International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/mseee-18.2018.44.

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Meyn, Larry. "Probabilistic Methods for Air Traffic Demand Forecasting." In AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4766.

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Reports on the topic "Demand forecasting methods"

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Kim, Sour, Phalla Chem, Sovannarith So, Sean Somatra Kim, and Sokhem Pech. Methods and Tools Applied for Climate Change Vulnerability and Adaptation Assessment in Cambodia’s Tonle Sap Basin. Cambodia Development Resource Institute, 2014. https://doi.org/10.64202/wp.97.201407.

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Cambodia is highly susceptible to natural disasters due to the frequency and intensity of extreme climatic events, primarily floods, droughts and windstorms, but also in particular to increased climate variability.These disasters and climate-related hazardshave exacted huge socioeconomic costs on the country’s economy and people’s livelihoods, especially in the last decade. It isimportant, therefore, to understand not only the level of impactbut also the vulnerability and the capacity of people to adapt to these hazards. This emphasises the needfor vulnerability and adaptation assessmentsthat
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Navarro, Adoracion, Ma Kristina Ortiz, and Jethro El Camara. How Energy Secure is the Philippines? Philippine Institute for Development Studies, 2023. http://dx.doi.org/10.62986/dp2023.15.

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Energy security is a comprehensive concept, and pursuing it as a developmental goal entails, first and foremost, a clearer understanding of what it means. This study proposes six broad elements in assessing energy security: sufficiency, reliability, resilience, affordability, accessibility, and sustainability. In assessing the energy security situation in the Philippines, the study employs an indicators-based assessment. Based on existing literature, the method involves defining specific energy security indicators (ESIs) corresponding to the six broad elements mentioned, running some calculati
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