Log in

Relevant bibliographies by topics / Methods of demand forecasting

Contents

Journal articles

Academic literature on the topic 'Methods of demand forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Methods of demand forecasting.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Methods of demand forecasting"

1

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Paliński, Andrzej. "Prognozowanie zapotrzebowania na gaz metodami sztucznej inteligencji." Nafta-Gaz 75, no. 2 (2019): 111–17. http://dx.doi.org/10.18668/ng.2019.02.07.

Full text

Abstract:

The paper presents contemporary trends in artificial intelligence and machine learning methods, which include, among others, artificial neural networks, decision trees, fuzzy logic systems and others. Computational intelligence methods are part of the field of research on artificial intelligence. Selected methods of computational intelligence were used to build medium-term monthly forecasts of natural gas demand for Poland. The accuracy of forecasts obtained using the artificial neural network and the decision tree with classical linear regression was compared based on historical data from a t

APA, Harvard, Vancouver, ISO, and other styles

5

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

Full text

Abstract:

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 presen

APA, Harvard, Vancouver, ISO, and other styles

6

Wu, Ping, Xiao Nian Sun, and Xian Guang Wang. "Research on New Ideas of Comprehensive Traffic Demand Analysis Techniques and Methods." Applied Mechanics and Materials 587-589 (July 2014): 2246–51. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.2246.

Full text

Abstract:

There are several misunderstandings in the current forecasting methods of integrated traffic demand in China, seriously impacting on the forecasting accuracy of integrated traffic demand. Starting from systems theory and the adaptive theory of traffic and economic development and combined with characteristics of integrated transportation demand, this paper proposes the innovative thinking of the analytical techniques and methods of integrated traffic demand in the future and the forecasting methods of integrated passenger and cargo transport demand as well as the model approach of the structur

APA, Harvard, Vancouver, ISO, and other styles

7

Apostol, Rostislav, Mariusz Łaciak, Andrìj Olìjnik, and Adam Szurlej. "Analysis of the methods for gas demand forecasting." AGH Drilling, Oil, Gas 34, no. 2 (2017): 429. http://dx.doi.org/10.7494/drill.2017.34.2.429.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Vikas, Udbhav, Karthik Sunil, Rohini S. Hallikar, Pattem Deeksha, and Dr Ramakanth Kumar P. "A Comprehensive Study on Demand Forecasting Methods and Algorithms for Retail Industries." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 409–20. http://dx.doi.org/10.51201/jusst/21/05283.

Full text

Abstract:

Without a doubt, demand forecasting is an essential part of a company’s supply chain. It predicts future demand and specifies the level of supply-side readiness needed to satisfy the demand. It is imperative that if a company’s forecasting isn’t reasonably reliable, the entire supply chain suffers. Over or under forecasted demand would have a debilitating impact on the operation of the supply chain, along with planning and logistics. Having acknowledged the importance of demand forecasting, one must look into the techniques and algorithms commonly employed to predict demand. Data mining, stati

APA, Harvard, Vancouver, ISO, and other styles

9

Jugović, Alen, Svjetlana Hess, and Tanja Poletan Jugović. "Traffic Demand Forecasting for Port Services." PROMET - Traffic&Transportation 23, no. 1 (2012): 59–69. http://dx.doi.org/10.7307/ptt.v23i1.149.

Full text

Abstract:

Successful management of any sea port depends primarily on the harmonisation of transport supply and demand, whereas their incompatibility leads to a number of problems. The port, i.e. its management, through its operation and part of port policy may affect the planning of the construction or modernization of its port facilities. In doing so, the specified planning requires forecasting and quantification of the needs for infrastructural services of specified port, i.e. assessment of traffic demand. Accordingly, the basic problem of research in this paper is forecasting of traffic demand for th

APA, Harvard, Vancouver, ISO, and other styles

10

Kon, Sen Cheong, and Lindsay W. Turner. "Neural Network Forecasting of Tourism Demand." Tourism Economics 11, no. 3 (2005): 301–28. http://dx.doi.org/10.5367/000000005774353006.

Full text

Abstract:

In times of tourism uncertainty, practitioners need short-term forecasting methods. This study compares the forecasting accuracy of the basic structural method (BSM) and the neural network method to find the best structure for neural network models. Data for arrivals to Singapore are used to test the analysis while the naïve and Holt-Winters methods are used for base comparison of simpler models. The results confirm that the BSM remains a highly accurate method and that correctly structured neural models can outperform BSM and the simpler methods in the short term, and can also use short data

APA, Harvard, Vancouver, ISO, and other styles

More sources

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!