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Journal articles on the topic 'Methods of demand forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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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.

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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.

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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.

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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.

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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

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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.

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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 presen

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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.

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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

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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.

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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.

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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

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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.

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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

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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.

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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

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11

Miao, Xin, and Bao Xi. "AGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND." TRANSPORT 23, no. 1 (2008): 26–30. http://dx.doi.org/10.3846/1648-4142.2008.23.26-30.

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The objective of this paper is to study the quantitative forecasting method for agile forecasting of logistics demand in dynamic supply chain environment. Characteristics of dynamic logistics demand and relative forecasting methods are analyzed. In order to enhance the forecasting efficiency and precision, extended Kalman Filter is applied to training artificial neural network, which serves as the agile forecasting algorithm. Some dynamic influencing factors are taken into consideration and further quantified in agile forecasting. Swarm simulation is used to demonstrate the forecasting results

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12

Panda, Sujit Kumar, Alok Kumar Jagadev, and Sachi Nandan Mohanty. "Forecasting Methods in Electric Power Sector." International Journal of Energy Optimization and Engineering 7, no. 1 (2018): 1–21. http://dx.doi.org/10.4018/ijeoe.2018010101.

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Electric power plays a vibrant role in economic growth and development of a region. There is a strong co-relation between the human development index and per capita electricity consumption. Providing adequate energy of desired quality in various forms in a sustainable manner and at a competitive price is one of the biggest challenges. To meet the fast-growing electric power demand, on a sustained basis, meticulous power system planning is required. This planning needs electrical load forecasting as it provides the primary inputs and enables financial analysis. Accurate electric load forecasts

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13

Hasni, M., M. S. Aguir, M. Z. Babai, and Z. Jemai. "Spare parts demand forecasting: a review on bootstrapping methods." International Journal of Production Research 57, no. 15-16 (2018): 4791–804. http://dx.doi.org/10.1080/00207543.2018.1424375.

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14

Donkor, Emmanuel A., Thomas A. Mazzuchi, Refik Soyer, and J. Alan Roberson. "Urban Water Demand Forecasting: Review of Methods and Models." Journal of Water Resources Planning and Management 140, no. 2 (2014): 146–59. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000314.

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15

Taylor, James W. "Triple seasonal methods for short-term electricity demand forecasting." European Journal of Operational Research 204, no. 1 (2010): 139–52. http://dx.doi.org/10.1016/j.ejor.2009.10.003.

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16

Mo, Yi Kui, Kai Wang, and Shen Lv. "Fuzzy Combination Forecasting of Urban Transit Demand." Applied Mechanics and Materials 744-746 (March 2015): 1808–12. http://dx.doi.org/10.4028/www.scientific.net/amm.744-746.1808.

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Based on the analysis of the existing forecasting methods of the urban public transit demand scale and concerning the characteristics of urban public transit demand forecasting, this paper introduces the concept of triangular fuzzy number and puts forward the fuzzy combination forecasting methods in terms of political factors. Following that, steps and process to implement the fuzzy combination forecasting are further expounded, and specific examples are adopted to prove the feasibility and validity of the method.

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17

Rodrigues, Lucas Lopes Filholino, Igor Henrique Inácio de Oliveira, Maurílio Fagundes Alexandre, Rodrigo Rodrigues Castorani, and Celso Jacubavicius. "Stocks management through application of demand forecast methods: a case study." Independent Journal of Management & Production 7, no. 5 (2016): 699. http://dx.doi.org/10.14807/ijmp.v7i5.458.

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The present study consists in assessing the feasibility of implementing demand forecasting techniques due to the optimization of inventory management, so that it is objective the reduce storage costs and to have the least amount of stationary material stock in a certain period. Data analysis was for application of techniques based on the real case of a multinational company in the segment of electronic and digital systems in the infrastructure area, which operates in the metropolitan region of São Paulo.The study aims to evaluate the behavior of the studied company demand, in order to demonstr

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18

Alasali, Feras, Husam Foudeh, Esraa Mousa Ali, Khaled Nusair, and William Holderbaum. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources." Energies 14, no. 8 (2021): 2151. http://dx.doi.org/10.3390/en14082151.

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More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Pr

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19

Lim, P. Y., and C. V. Nayar. "Solar Irradiance and Load Demand Forecasting based on Single Exponential Smoothing Method." International Journal of Engineering and Technology 4, no. 4 (2012): 451–55. http://dx.doi.org/10.7763/ijet.2012.v4.408.

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20

Tsai, Yihjia, Kuan-Wu Chang, Giou-Teng Yiang, and Hwei-Jen Lin. "Demand Forecast and Multi-Objective Ambulance Allocation." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 07 (2018): 1859011. http://dx.doi.org/10.1142/s0218001418590115.

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This study considers the two-fold dynamic ambulance allocation problem, which includes forecasting the distribution of Emergency Medical Service (EMS) requesters and allocating ambulances dynamically according to the predicted distribution of requesters. EMSs demand distribution forecasting is based on on-record historical demands. Subsequently, a multi-objective ambulance allocation model (MOAAM) is solved by a mechanism called Jumping Particle Swarm Optimization (JPSO) according to the forecasted distribution of demands. Experiments were conducted using recorded historical data for EMS reque

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21

Voon, Derby, and James Fogarty. "A Note on Forecasting Alcohol Demand." Journal of Wine Economics 14, no. 2 (2019): 208–13. http://dx.doi.org/10.1017/jwe.2019.15.

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AbstractA recent study in the Journal of Wine Economics presented forecasts of future alcohol consumption derived using the ARIMA (Box–Jenkins) method. Alcohol consumption forecasts can be developed using many different methodologies. In this Note we highlight the value of using multiple methods to develop alcohol consumption forecasts, and demonstrate the capability of the R software platform as a general forecasting tool. (JEL Classifications: D12, C53)

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22

Gui, Xiang Quan, Li Li, Peng Shou Xie, and Jie Cao. "Using a Combined Method to Forecasting Electricity Demand." Applied Mechanics and Materials 678 (October 2014): 120–25. http://dx.doi.org/10.4028/www.scientific.net/amm.678.120.

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In electric market, accurate electricity demand forecasting is often needed. Because electricity demand forecasting has become needful for creators and purchasers in the electric markets at present. But in electricity demand forecasting, noise signals, caused by various unstable factors, often corrupt demand series. In order to seek accurate demand forecasting methods, this article proposed a new combined electric load forecasting method (WSENN) which based on Wavelet Transform (WT), Seasonal Adjustment (SA) and Elman Neural Network (ENN) to forecast electricity demand. The effectiveness of WS

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23

An, Yeqi, Yulin Zhou, and Rongrong Li. "Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods." Energies 12, no. 13 (2019): 2574. http://dx.doi.org/10.3390/en12132574.

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With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated mov

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24

Goodrich, R. L., R. K. Mehra, R. F. Engle, and C. W. J. Granger. "Exploration and Comparison of New Methods for Electric Demand Forecasting." IFAC Proceedings Volumes 18, no. 5 (1985): 1045–53. http://dx.doi.org/10.1016/s1474-6670(17)60700-6.

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25

Ferbar, Liljana, David Čreslovnik, Blaž Mojškerc, and Martin Rajgelj. "Demand forecasting methods in a supply chain: Smoothing and denoising." International Journal of Production Economics 118, no. 1 (2009): 49–54. http://dx.doi.org/10.1016/j.ijpe.2008.08.042.

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26

Nafil, Abdellah, Mostafa Bouzi, Kamal Anoune, and Naoufl Ettalabi. "Comparative study of forecasting methods for energy demand in Morocco." Energy Reports 6 (November 2020): 523–36. http://dx.doi.org/10.1016/j.egyr.2020.09.030.

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27

Bünning, Felix, Philipp Heer, Roy S. Smith, and John Lygeros. "Improved day ahead heating demand forecasting by online correction methods." Energy and Buildings 211 (March 2020): 109821. http://dx.doi.org/10.1016/j.enbuild.2020.109821.

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28

Min, Jingjing, Yan Dong, Fang Wu, Naijie Li, and Hua Wang. "Comparative Analysis of Two Methods of Natural Gas Demand Forecasting." IOP Conference Series: Earth and Environmental Science 632 (January 14, 2021): 032033. http://dx.doi.org/10.1088/1755-1315/632/3/032033.

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29

De Nicolao, Giuseppe, Emanuele Fabbiani, and Andrea Marziali. "Ensembling methods for countrywide short-term forecasting of gas demand." International Journal of Oil, Gas and Coal Technology 26, no. 2 (2021): 184. http://dx.doi.org/10.1504/ijogct.2021.10035077.

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30

Marziali, Andrea, Emanuele Fabbiani, and Giuseppe De Nicolao. "Ensembling methods for countrywide short-term forecasting of gas demand." International Journal of Oil, Gas and Coal Technology 26, no. 2 (2021): 184. http://dx.doi.org/10.1504/ijogct.2021.112874.

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31

Le, Tuan Ho, Quang Hung Le, and Thanh Hoang Phan. "A comparative study of short-term load forecasting methods in distribution network." Journal of Science, Quy Nhon University 15, no. 1 (2021): 23–35. http://dx.doi.org/10.52111/qnjs.2021.15103.

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Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecas

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32

Sani, B., and B. G. Kingsman. "Selecting the Best Periodic Inventory Control and Demand Forecasting Methods for Low Demand Items." Journal of the Operational Research Society 48, no. 7 (1997): 700. http://dx.doi.org/10.2307/3010059.

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33

Sani, B., and B. G. Kingsman. "Selecting the best periodic inventory control and demand forecasting methods for low demand items." Journal of the Operational Research Society 48, no. 7 (1997): 700–713. http://dx.doi.org/10.1038/sj.jors.2600418.

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34

Sani, B., and B. G. Kingsman. "Selecting the best periodic inventory control and demand forecasting methods for low demand items." Journal of the Operational Research Society 48, no. 7 (1997): 700–713. http://dx.doi.org/10.1057/palgrave.jors.2600418.

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35

Suhartono, Suhartono, Salafiyah Isnawati, Novi Ajeng Salehah, Dedy Dwi Prastyo, Heri Kuswanto, and Muhammad Hisyam Lee. "Hybrid SSA-TSR-ARIMA for water demand forecasting." International Journal of Advances in Intelligent Informatics 4, no. 3 (2018): 238. http://dx.doi.org/10.26555/ijain.v4i3.275.

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Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA componen

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36

Wang, Zi-jia, Hai-xu Liu, Shi Qiu, Ji-ping Fang, and Ting Wang. "The Predictability of Short-Term Urban Rail Demand: Choice of Time Resolution and Methodology." Sustainability 11, no. 21 (2019): 6173. http://dx.doi.org/10.3390/su11216173.

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The accuracy of short-term demand forecasting is critical for real-time operation management of urban rail transit, which largely depends on the choice of time resolution. Although there have been continuous improvements in forecasting models, the basic issue has not been well addressed. In this regard, the predictability of short-term demand in terms of time resolution setting and the corresponding model selection have been addressed in this study. Two methods have been considered: the demand forecasting with the past demand during the same time slot on the same weekday (the same period metho

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37

Ogcu Kaya, Gamze, and Omer Fahrettin Demirel. "Parameter optimization of intermittent demand forecasting by using spreadsheet." Kybernetes 44, no. 4 (2015): 576–87. http://dx.doi.org/10.1108/k-03-2015-0062.

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Purpose – Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization. Design/methodology/approach – Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods fr

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38

Savage, Joseph P. "Simplified Approaches to Ferry Travel Demand Forecasting." Transportation Research Record: Journal of the Transportation Research Board 1608, no. 1 (1997): 17–29. http://dx.doi.org/10.3141/1608-03.

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Large ferry systems and ferry systems operating in major urban areas can often rely on regional travel models using the traditional four-step travel demand forecasting process to predict ridership levels for their routes. However, smaller agencies and agencies in rural areas often do not have either the data or the resources to develop and implement complex forecasting models for their systems. A variety of simplified forecasting methods are reviewed that can be implemented with inexpensive, commercially available software using ferry traffic counts and readily available socioeconomic data fro

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39

Ruiz-Abellón, María Carmen, Luis Alfredo Fernández-Jiménez, Antonio Guillamón, Alberto Falces, Ana García-Garre, and Antonio Gabaldón. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation." Energies 13, no. 1 (2019): 11. http://dx.doi.org/10.3390/en13010011.

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The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been propo

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40

Ma, Junhai, and Xiaogang Ma. "A Comparison of Bullwhip Effect under Various Forecasting Techniques in Supply Chains with Two Retailers." Abstract and Applied Analysis 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/796384.

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We examine the impact of three forecasting methods on the bullwhip effect in a two-stage supply chain with one supplier and two retailers. A first order mixed autoregressive-moving average model (ARMA(1, 1)) performs the demand forecast and an order-up-to inventory policy characterizes the inventory decision. The bullwhip effect is measured, respectively, under the minimum mean-squared error (MMSE), moving average (MA), and exponential smoothing (ES) forecasting techniques. The effect of parameters on the bullwhip effect under three forecasting methods is analyzed and the bullwhip effect under

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41

Nekrasova, T., S. Pupentsova, and E. Aksenova. "METHODS FOR ESTIMATION AND FORECASTING SUPPLY AND DEMAND FOR TELECOMMUNICATION SERVICES." Transbaikal State University Journal 24, no. 10 (2018): 108–16. http://dx.doi.org/10.21209/2227-9245-2018-24-10-108-116.

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42

Doi, Toshiaki, and Yozo Shibata. "Study on effectiveness of demand forecasting methods applied to Tokaido Shinkansen." Doboku Gakkai Ronbunshu, no. 562 (1997): 121–31. http://dx.doi.org/10.2208/jscej.1997.562_121.

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43

Pivkin, K. S. "Realization of Regression Methods of Demand Forecasting Using the R Language." Intellekt. Sist. Proizv. 16, no. 1 (2018): 15. http://dx.doi.org/10.22213/2410-9304-2018-1-15-25.

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Рассматривается регрессионный анализ как ключевой метод прогнозирования величины товарного спроса. Приводится список методов, являющихся наиболее эффективными для расчета оценки прогноза: линейная регрессия с регуляризацией, регрессия на основе опорных векторов, метод случайного леса. Необходимые расчеты реализуются на языке программирования R с использованием как базового функционала, так и расширений, которые обеспечивают возможность использования рассматриваемых методов. В качестве входящих данных используются показатели работы магазина и товарные характеристики. Определяется метрика качест

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44

Hasni, M., M. S. Aguir, M. Z. Babai, and Z. Jemai. "On the performance of adjusted bootstrapping methods for intermittent demand forecasting." International Journal of Production Economics 216 (October 2019): 145–53. http://dx.doi.org/10.1016/j.ijpe.2019.04.005.

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45

D'Amico, A., G. Ciulla, L. Tupenaite, and A. Kaklauskas. "Multiple criteria assessment of methods for forecasting building thermal energy demand." Energy and Buildings 224 (October 2020): 110220. http://dx.doi.org/10.1016/j.enbuild.2020.110220.

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46

Ghalehkhondabi, Iman, Ehsan Ardjmand, Gary R. Weckman, and William A. Young. "An overview of energy demand forecasting methods published in 2005–2015." Energy Systems 8, no. 2 (2016): 411–47. http://dx.doi.org/10.1007/s12667-016-0203-y.

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47

Vilar, Juan M., Ricardo Cao, and Germán Aneiros. "Forecasting next-day electricity demand and price using nonparametric functional methods." International Journal of Electrical Power & Energy Systems 39, no. 1 (2012): 48–55. http://dx.doi.org/10.1016/j.ijepes.2012.01.004.

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48

Firat, Murat, Derya Yiltas-Kaplan, and Ruya Samli. "Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods." JUCS - Journal of Universal Computer Science 27, no. 6 (2021): 564–81. http://dx.doi.org/10.3897/jucs.68185.

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Over the past decades, air transportation has expanded and big data for transportation era has emerged. Accurate travel demand information is an important issue for the transportation systems, especially for airline industry. So, &ldquo;optimal seat capacity problem between origin and destination pairs&rdquo; which is related to the load factor must be solved. In this study, a method for determining optimal seat capacity that can supply the highest load factor for the flight operation between any two countries has been introduced. The machine learning methods of Artificial Neural Netwo

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49

Sulistyo, Sinta Rahmawidya, and Alvian Jonathan Sutrisno. "LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP." Angkasa: Jurnal Ilmiah Bidang Teknologi 10, no. 2 (2018): 107. http://dx.doi.org/10.28989/angkasa.v10i2.362.

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Lumpy demand represents the circumstances when a demand for an item has a large proportion of periods having zero demand. This certain situation makes the time series methods might become inappropriate due to the model’s inability to capture the demand pattern. This research aims to compare several forecasting methods for lumpy demand that is represented by the demand of spare part. Three forecasting methods are chosen; Linear Exponential Smoothing (LES), Artificial Neural Network (ANN), and Bootstrap. The Mean Absolute Scaled Error (MASE) is used to measure the forecast performance. In order

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de Souza Groppo, Gustavo, Marcelo Azevedo Costa, and Marcelo Libânio. "Predicting water demand: a review of the methods employed and future possibilities." Water Supply 19, no. 8 (2019): 2179–98. http://dx.doi.org/10.2166/ws.2019.122.

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Abstract The balance between water supply and demand requires efficient water supply system management techniques. This balance is achieved through operational actions, many of which require the application of forecasting concepts and tools. In this article, recent research on urban water demand forecasting employing artificial intelligence is reviewed, aiming to present the ‘state of the art’ on the subject and provide some guidance regarding methods and models to research and professional sanitation companies. The review covers the models developed using standard statistical techniques, such

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