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Journal articles on the topic 'Forecasting – Research'

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Published: 25 July 2024

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1

Zheng, Xiao Xia, and Fu Yang. "Research of Wind Speed and Wind Power Forecasting." Advanced Materials Research 347-353 (October 2011): 611–14. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.611.

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Wind power has entered a rapid progress stage. Due to the intermittency of wind energy and the non-linearity of power system, there exist many uncertain variables which should be considered in the wind power prediction. Accurate wind power forecastings are beneficial for wind plant operators, utility operators, and utility customers. The current forecasting methods include persistence method, physical method, statistical method, and the comprehensive one combing all the other methods. This paper provides a detail review on wind speed and wind power forecasting methods based on recent available published papers. Several forecasting models were discussed and a lot of researchers on the models, which have their own characteristics, were presented. An overview of comparative analysis of wind forecasting time scales is discussed as well.

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2

Diamantopoulos, A. "Research on Forecasting." International Journal of Forecasting 18, no. 3 (July 2002): 479–80. http://dx.doi.org/10.1016/s0169-2070(02)00003-1.

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3

Adya, Monica. "Research on Forecasting." International Journal of Forecasting 18, no. 3 (July 2002): 481–82. http://dx.doi.org/10.1016/s0169-2070(02)00005-5.

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4

Chatfield, Chris. "Research on Forecasting." International Journal of Forecasting 15, no. 2 (April 1999): 225–26. http://dx.doi.org/10.1016/s0169-2070(98)00078-8.

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5

Sanders, Nada R. "Research on Forecasting." International Journal of Forecasting 15, no. 3 (July 1999): 345–46. http://dx.doi.org/10.1016/s0169-2070(99)00010-2.

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6

Fildes, Robert. "Research on forecasting." International Journal of Forecasting 5, no. 1 (January 1989): 151–53. http://dx.doi.org/10.1016/0169-2070(89)90081-2.

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7

Vasilevskaya, L. N., and E. Yu Potalova. "Hydrometeorological research and forecasting." Hydrometeorological research and forecasting, no. 1 (March 20, 2020): 6–20. http://dx.doi.org/10.37162/2618-9631-2020-1-6-20.

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8

Scott Armstrong, J. "Research needs in forecasting." International Journal of Forecasting 4, no. 3 (January 1988): 449–65. http://dx.doi.org/10.1016/0169-2070(88)90111-2.

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9

Evanschitzky, Heiner, and J. Scott Armstrong. "Replications of forecasting research." International Journal of Forecasting 26, no. 1 (January 2010): 4–8. http://dx.doi.org/10.1016/j.ijforecast.2009.09.003.

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10

Boylan, John E., Paul Goodwin, Maryam Mohammadipour, and Aris A. Syntetos. "Reproducibility in forecasting research." International Journal of Forecasting 31, no. 1 (January 2015): 79–90. http://dx.doi.org/10.1016/j.ijforecast.2014.05.008.

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11

Raupp, Edward, and Nato Apkhazava. "New Research in Forecasting." Caucasus Journal of Social Sciences 2, no. 1 (November 10, 2023): 28–36. http://dx.doi.org/10.62343/cjss.2009.16.

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All institutions in Georgia are changing. Decision-makers in Government agencies and private organizations need the best available data and analyses to meet the challenges of changing times. Evidence-based forecasting can help officials to predict the likely outcomes of their decisions. This paper reports on work being done by THE GEORGIA FORECASTTM and The University of Georgia Forecasting Center to identify and apply modern methods to Georgia’s most pressing issues. These methods include statistical techniques, such as time series and regression, and judgmental adjustments, such as Delphi, surveys of consumers and producers, and scenario scripting. One recent application studied in this research project, forecasting tourism in Georgia, is an example of how modern methods may improve decision-making by those who make public policy, allocate resources, and make business decisions.Statistical forecasting techniques assume that the near future will look substantially like the recent past. We can, therefore, use data from recent history to make predictions about the near future. The longer the horizon, the weaker the assumption. Two general classes of statistical forecasts are (1) Causal and (2) Non-Causal. The most common causal model is regression: the variable to be determined (Y) is related to one or more predictor variables (Xi). For example, the quantity demanded of a good (Qd) may be estimated from the price of that good (P) and other factors, such as income, relative prices of substitute and complementary goods, weather, etc. Time series models are non-causal. We may not know why a variable of interest rises or falls, but we can detect patterns from the recent past, such as trend, seasonality, and cycles. Judgmental methods assume some degree of knowledge that may not bereflected in the statistics. These may include, among others, a Delphi panel of experts, survey of consumers and producers, game theory, and scenario scripting. Because of the high degree of uncertainty in the Georgian economic, political, and social environment, the use of one model is unlikely to yield reliable forecasts. Therefore, THE GEORGIA FORECASTTM and The University of GeorgiaForecasting Center use a combination of methods and continually evaluate the performance of their forecasts. This paper uses the case of forecasting tourism to demonstrate these techniques.

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Shalaev, V. S., S. N. Rykunin, and V. I. Melekhov. "FORECASTING RESEARCH OF «FOREST PRODUCTS»." FORESTRY BULLETIN 23, no. 131 (February 2019): 110–17. http://dx.doi.org/10.18698/2542-1468-2019-1-110-117.

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13

Sigalov, Konstantin E., and Mikhail V. Fedorov. "Forecasting in Historical Legal Research." History of state and law 4 (April 29, 2021): 32–37. http://dx.doi.org/10.18572/1812-3805-2021-4-32-37.

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History is the science of events unfolding in time, and the past, present, and future are equally the goal of truly scientific historical research. The foresight of historical phenomena is conditioned by the authenticity of historical sources, the adequacy of the interpretation of facts, and a strict scientific methodology.

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14

Fildes, Robert. "RESEARCH ISSUES IN BUSINESS FORECASTING." Management Research News 11, no. 4/5 (April 1988): 2–5. http://dx.doi.org/10.1108/eb027980.

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15

Hackman, Robert M., Bharat B. Aggarwal, Rhona S. Applebaum, Ralph W. deVere White, Michael A. Dubick, David Heber, Toshinori Ito, et al. "Forecasting Nutrition Research in 2020." Journal of the American College of Nutrition 33, no. 4 (July 4, 2014): 340–46. http://dx.doi.org/10.1080/07315724.2014.943113.

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16

Jennings, Linda W., and Dean M. Young. "Forecasting methods for institutional research." New Directions for Institutional Research 1988, no. 58 (1988): 77–96. http://dx.doi.org/10.1002/ir.37019885808.

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17

Almgren, Ann, Aaron Lattanzi, Riyaz Haque, Pankaj Jha, Branko Kosovic, Jeffrey Mirocha, Bruce Perry, et al. "ERF: Energy Research and Forecasting." Journal of Open Source Software 8, no. 87 (July 1, 2023): 5202. http://dx.doi.org/10.21105/joss.05202.

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18

Udhaya, P. "Improving the Reproducibility in Forecasting Research." International Journal of Research and Review 10, no. 7 (July 28, 2023): 950–58. http://dx.doi.org/10.52403/ijrr.202307110.

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The value of reproduction is recognized in many scientific fields. Reproducibility is a necessary condition for reliability because the inability to reproduce results indicates that methods are not sufficiently specified, thus preventing replication. This article describes how two independent teams of researchers attempted to reproduce the empirical findings of an important study, “Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy” (Miller & Williams, 2003, IJF). Teams of researchers proceeded systematically, reporting results before and after receiving clarifications. These inconsistencies have led to differences in conclusions about the conditions under which seasonal damping surpasses classical decay. The study addresses forecasting methods using a flow chart. It is argued that this approach to method documentation complements the provision of computer code by being accessible to a wider audience of forecasting practitioners and researchers. The importance of this research lies not only in its lessons for seasonal forecasting, but also in its approach to the reproduction of forecasting research. Keywords: Forecasting practice, Reproduction, Seasonal Forecasting, Empirical analysis

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19

Zhen, Zhao, Zheng Wang, Fei Wang, Zengqiang Mi, and Kangping Li. "Research on a cloud image forecasting approach for solar power forecasting." Energy Procedia 142 (December 2017): 362–68. http://dx.doi.org/10.1016/j.egypro.2017.12.057.

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20

Li, Gang, and Xiaoying Jiao. "Tourism forecasting research: a perspective article." Tourism Review 75, no. 1 (January 15, 2020): 263–66. http://dx.doi.org/10.1108/tr-09-2019-0382.

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Purpose The purpose of this paper is to provide a short review of tourism forecasting literature and general summary of the trends and developments in tourism forecasting and point out directions for future research in the next 75 years. Design/methodology/approach This is a general literature overview. Findings Key trends are identified for next 75 years. Originality/value First overview in tourism forecasting that provides foresight on long-term future trends (over next 75 years).

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21

Nikolaeva, M. A., and I. B. Torotenkova. "Forecasting methods in marketing and commodity research activities." Tovaroved prodovolstvennykh tovarov (Commodity specialist of food products), no. 3 (March 1, 2022): 180–89. http://dx.doi.org/10.33920/igt-01-2203-04.

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The article considers the relevance of forecasting in commodity research and marketing activities. A classification of forecasting methods is presented and a brief description of the most appropriate forecasting methods in commodity research and marketing activities is given.

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22

Kalachikhin, P. A. "Forecasting Basic Research Using Scientometric Data." Scientific and Technical Information Processing 47, no. 2 (April 2020): 126–32. http://dx.doi.org/10.3103/s0147688220020100.

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23

Gruetzemacher, Ross, Florian E. Dorner, Niko Bernaola-Alvarez, Charlie Giattino, and David Manheim. "Forecasting AI progress: A research agenda." Technological Forecasting and Social Change 170 (September 2021): 120909. http://dx.doi.org/10.1016/j.techfore.2021.120909.

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24

Xiaoping, Xue, Li Hongyi, and Li Nan. "Heliogreenhouse Ground Temperature Forecasting Technology Research." Advance Journal of Food Science and Technology 12, no. 4 (October 5, 2016): 161–67. http://dx.doi.org/10.19026/ajfst.12.2894.

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Xiaoping, Xue, Li Nan, Li Hongyi, and Cao Jie. "Heliogreenhouse Air Temperature Forecasting Technology Research." Advance Journal of Food Science and Technology 5, no. 8 (August 5, 2013): 995–1001. http://dx.doi.org/10.19026/ajfst.5.3195.

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26

Fildes, R., K. Nikolopoulos, S. F. Crone, and A. A. Syntetos. "Forecasting and operational research: a review." Journal of the Operational Research Society 59, no. 9 (September 2008): 1150–72. http://dx.doi.org/10.1057/palgrave.jors.2602597.

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27

Lawrence, Kenneth D. "Market research using forecasting in business." International Journal of Forecasting 9, no. 4 (December 1993): 579–80. http://dx.doi.org/10.1016/0169-2070(93)90083-y.

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28

Simpson, Sarah. "From Research Model to Forecasting Tool." Space Weather 1, no. 1 (October 2003): n/a. http://dx.doi.org/10.1029/2003sw000029.

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29

&NA;. "Forecasting the future of medical research." Inpharma Weekly &NA;, no. 1275 (February 2001): 3. http://dx.doi.org/10.2165/00128413-200112750-00004.

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30

Wang, Jianzhou, Haipeng Zhang, Qiwei Li, and Aini Ji. "Design and research of hybrid forecasting system for wind speed point forecasting and fuzzy interval forecasting." Expert Systems with Applications 209 (December 2022): 118384. http://dx.doi.org/10.1016/j.eswa.2022.118384.

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31

Hitam, Nor Azizah, and Amelia Ritahani Ismail. "Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (September 1, 2018): 1121. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp1121-1128.

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Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

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32

Brown, Lawrence D. "Earnings forecasting research: its implications for capital markets research." International Journal of Forecasting 9, no. 3 (November 1993): 295–320. http://dx.doi.org/10.1016/0169-2070(93)90023-g.

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33

Xi, Jia, and Ping Ba Sha. "Research on Optimization of Inventory Management Based on Demand Forecasting." Applied Mechanics and Materials 687-691 (November 2014): 4828–31. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4828.

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Demand forecasting is the basis of the inventory management. Aiming at the problem of subjective forecasting method, we use quadratic exponential smoothing method to establish the mathematical model, to forecast sales volume of product A in every month in 2013. And based on demand forecasting, we put forward ABC classification management method to solve the inventory management issues. The research result of this paper has important implications in improving the inventory management level for many enterprises.Demand Forecasting and Inventory ManagementInventory management is an important part of enterprise management, and it directly affects the business situation of enterprises. A reasonable inventory can significantly enhance the comprehensive competitiveness of enterprises; too much or too little inventory settings would have a bad impact on the business, and some company even bog down because of inventory problems companies bogged down because of inventory problems [1-2]. To do inventory management, what should we do in the first step. The answer is demand forecast. When business scale reaches a certain level, it would need strict, systematic demand forecasting. The more accurate the demand forecasting is, the more accurate inventory planning would be, and more favorable for business enterprises.Few companies are able to be completely in accordance with the order production, and the vast majority of businesses are not waiting for orders after arrival, then determine how much raw material and manpower needed, and how to arrange production. Because it often takes a long production cycle, and no one is willing to wait a month to buy a bag of washing powder. Successful companies always make accurate predictions for product demand, and then put into production according to forecasting [3]. Due to their more accurate predictions, they can often carry out a reasonable plan and inventory management. Inventory forecasting, its essence is demand forecasting [4]. Demand forecasting provides important information for inventory management such as inventory amount, lead time, inventory turns. Demand forecasting is based on research and statistics, to make a scientific and reasonable inference for product demand. Product demand generally is within a certain period, certain market range, the number of consumers’ demand for a product. Demand forecasting results can help companies determine the amount of raw material inventory and products, and provide enterprise continuous production of raw materials needed, save liquidity and reduce inventory costs, improving the comprehensive competitiveness of enterprises.Product Demand Forecasting Model

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34

D. S. Mukashev and G. А. Abitova. "RESEARCH OF INFORMATION SYSTEMS IN WEATHER FORECASTING." Bulletin of Toraighyrov University. Physics & Mathematics series, no. 4,2023 (December 29, 2023): 19–32. http://dx.doi.org/10.48081/myhj7815.

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Weather forecasting plays a crucial role in numerous industries and activities, ranging from agriculture and energy to tourism and transportation. In recent years, information technologies have significantly enhanced the capabilities of weather forecasting, providing more accurate and timely data. This article explores innovative information technologies employed in weather forecasting and their impact on modern practices. It highlights the utilization of cloud computing and data storage for managing vast amounts of meteorological data, enabling the use of more precise forecasting models. Additionally, the article discusses the integration of Internet of Things (IoT) and sensor networks, which facilitate the collection of weather data from diverse sources and contribute to localized and real-time weather predictions. Artificial intelligence (AI) and machine learning techniques are also examined for their ability to analyze large datasets, identify patterns, and improve forecast accuracy. Finally, the article emphasizes the importance of advanced data visualization techniques in effectively conveying weather information to end-users. By harnessing these information technologies, weather forecasting continues to advance, empowering various industries and enhancing decision-making processes. Keywords: Cloud computing, Data storage, Internet of Things (IoT), Artificial intelligence (AI), Machine learning, Forecast accuracy, Data visualization, Decision-making processes.

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35

Wang, Yaoying, Shudong Sun, and Zhiqiang Cai. "The Research of Short-term Electric Load Forecasting based on Machine Learning Algorithm." Advances in Engineering Technology Research 4, no. 1 (March 22, 2023): 334. http://dx.doi.org/10.56028/aetr.4.1.334.2023.

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The ability of electric load forecasting become the key to measuring electric planning and dispatching, and improving the accuracy of electric load forecasting has become one hot spot topic of scholars in recent years. The traditional electric load forecasting algorithm mainly include statistical learning algorithm. The electric load forecasting algorithm based on traditional forecasting algorithm, machine learning algorithm and neural network algorithm greatly improve the convergence approximation effect and accuracy. In recent years, the electric load forecasting algorithms based on the deep learning algorithm has greatly reduced the error and improved the measurement accuracy and robustness, such as RNN, GRU, LSTM, DBN, TCN, and so on, as well as the combination algorithm based on deep learning. This paper introduces the classical algorithm of deep learning in electric load forecasting. The main purpose is to explore the error and fitting degree of algorithms established by different algorithms, and provide reference for the selection of forecasting algorithms for various types of electric load forecasting.

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36

Feng, Haoxuan. "Load Forecasting Research of Markov Chain based on Data Modeling." Journal of Physics: Conference Series 2470, no. 1 (March 1, 2023): 012001. http://dx.doi.org/10.1088/1742-6596/2470/1/012001.

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Abstract The introduction of the electricity market plays an irreplaceable role in assisting the power industry to achieve stable operation and planning of the power grid, promoting the effective use and dispatch of electric energy, and avoiding the waste of resources. The role of load forecasting for the electricity market is self-evident, in the context of big data, by building a load forecasting model based on the Markov chain, and using the actual data of a certain region for simulation calculation. The results show that the Markov forecasting method can play a good role in medium and long-term power load forecasting, and can be used as a supplement to conventional load forecasting.

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37

Miao, Zhongli, and Youhong Hu. "Research and Analysis of Combination Forecasting Model in Sports Competition." Journal of Sensors 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/5945599.

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With the increase of sports industry and various sports events, forecasting methods play an irreplaceable role in the competition system. At the same time of prediction, the selected calculation method, implementation scheme, model establishment, and other key implementation aspects have high technical requirements. In the whole prediction model, how to solve the problem of competition development is predicted and analyzed, and the best solution is selected for screening and evaluation, so as to significantly improve the prediction accuracy of the whole model. Understand the cause of the problem and solve it. Second, in the process of solving the problem, use the relevant forecasting technology theory to determine the weighted weight coefficient method of the combination forecasting model. In this paper, before the competition, select the best combination of forecasting model to sports-related personnel simulation cases and form a comparative analysis. Finally, through the combination of prediction experimental methods for the effective results of the problem, and in the later development process to get a new prediction model. In the actual process of forecasting, facing the complex combination of forecasting systems, the selected evaluation theme and the uncertainty of objects will produce great forecasting errors. Through excellent improvement, the defects of the combined forecasting model have been overcome, and the forecasting accuracy has been improved, which will greatly enhance the good development of physical education. The coordination mechanism, guarantee mechanism, and competition organization mechanism of sports competition alliance should be analyzed through prediction model. Spread Chinese sports culture, improve the level of sports competition, and carry out research and analysis on the prediction model of sports competition. The experimental results in this paper show that (1) the prediction process is generally tested in extremely unstable environment, so it will have a certain impact on the prediction accuracy, that is, there are data with the highest measurement accuracy of 0.99 and the lowest measurement accuracy of 0.92. (2) Different calculation methods will be selected for different prediction models of competitions. For example, the error coefficients of SSE are 1.6859, 1.8338, and 1.6161, respectively, which proves that different models have different contents in prediction. (3) The comprehensive promotion of sports competition will need more prediction models to select and promote. In the progress of the times, the prediction value shows an increasing trend, from 2.4 billion cubic meters to 3.3 billion cubic meters, which is the perfect realization of the prediction model. (4) In the structure of the forecasting model, the weighted geometric combination forecasting model is obtained by the statistical investigation and analysis of relevant personnel, which is the best combination forecasting model of sports competition with the optimal weight coefficient of 1.9, the forecasting value of 33.95, and the forecasting accuracy of 0.9996.

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38

Luo, Tian, Daofang Chang, and Zhenyu Xu. "Research on Apparel Retail Sales Forecasting Based on xDeepFM-LSTM Combined Forecasting Model." Information 13, no. 10 (October 15, 2022): 497. http://dx.doi.org/10.3390/info13100497.

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Accurate sales forecasting can provide a scientific basis for the management decisions of enterprises. We proposed the xDeepFM-LSTM combined forecasting model for the characteristics of sales data of apparel retail enterprises. We first used the Extreme Deep Factorization Machine (xDeepFM) model to explore the correlation between the sales influencing features as much as possible, and then modeled the sales prediction. Next, we used the Long Short-Term Memory (LSTM) model for residual correction to improve the accuracy of the prediction model. We then designed and implemented comparison experiments between the combined xDeepFM-LSTM forecasting model and other forecasting models. The experimental results show that the forecasting performance of xDeepFM-LSTM is significantly better than other forecasting models. Compared with the xDeepFM forecasting model, the combined forecasting model has a higher optimization rate, which provides a scientific basis for apparel companies to make adjustments to adjust their demand plans.

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Bakri, Rizal, Umar Data, and Andika Saputra. "Marketing Research : The Application of Auto Sales Forecasting Software to Optimize Product Marketing Strategies." Journal of Applied Science, Engineering, Technology, and Education 1, no. 1 (November 10, 2019): 6–12. http://dx.doi.org/10.35877/454ri.asci1124.

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The aims of this study is to apply the Auto Sales Forecasting software to predict sales transaction data. The Auto Sales Forecasting software consists of two main features namely descriptive analysis and forcasting features along with its visualization. Forecasting methods contained in the Auto Sales Forecasting application are forecasting methods of Simple Moving Average, Robust Exponantial Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. The Auto Sales Forecasting software can intelligently choose the best forecasting method based on RMSE values. The results showed that the Auto Sales Forecasting software successfully analyzed the sales transaction data. From the analysis it was found that there were 43 types of products produced and sold by the Futry Bakery & Cake Store. Three of them are the types of products that are most in demand by consumers, namely Sweet Bread, Maros Bread, and Traditional Cakes 3500. The best selling product type, Sweet Bread, is used to build forecasting models. The best forecasting method is the Robust Exponential Smoothing method with the smallest RMSE value of 0.83 on the variable number of sold out products. Forecasting results using the Robust Exponantial Smoothing method show that the average number of products to sell for the next seven days ranges from 116 products with a certain confidence interval value.

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Wang, Ting Ting, and Hong Yan Tan. "Research on City Gas Load Forecasting Method." Applied Mechanics and Materials 405-408 (September 2013): 2986–89. http://dx.doi.org/10.4028/www.scientific.net/amm.405-408.2986.

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The paper mainly studied the gas forecasting in gas-consuming rush hour and long-term load forecasting.The paper analyzed the factors influencing the volume of gas forecasting in rush hour and forecast the volume of gas in rush hour using fuzzy method and RBF neural network. In long term city gas load forecasting, there are some characters such as longer time and uncertain demand increasing. A method is proposed to weakening the original data by using buffer operator before use GM(1,1)model. It indicated that the result acquired by these methods was satisfied with the requirement of engineering, and it was helpful to dispatchers.

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41

Wu, Cheng Ming, Wei Wei Yao, and Wen Jing Dong. "Research of Load Forecasting Based on General Regression Neural Network." Advanced Materials Research 732-733 (August 2013): 926–29. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.926.

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Load forecasting technology is an important guarantee of the safe and steady operation in power system. Based on the analyzing and designing the network structure of GRNN, this paper sets an appropriate smoothing parameter and proposes a strategy for load forecasting under considering the weather factors. A short-term load forecasting model with the factors of temperature, humidity, wind speed, barometric pressure and rainfall is established. And the model can achieve the expected result after a complete test. Finally, comparing with the forecast model based on BP neural network, GRNN method shows the great superiority in power load forecasting.

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42

Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 869–72. http://dx.doi.org/10.22214/ijraset.2021.38069.

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Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction

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43

He, Xiang Shuo, Li Yang, and Xiao Na Yu. "Research of Combined Optimum Grey Model to Mid and Long Term Electric Load Forecasting." Advanced Materials Research 986-987 (July 2014): 1379–82. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1379.

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It is well known that mid and long term electric load forecasting has many uncertain factors that influence the forecasting precision greatly, so every forecasting method has its limitation. Considering limitations of basic grey model and conventional improved models, a new practical method called combined optimum grey model for mid and long term load forecasting is introduced. The combined model is composed of partial error optimum grey model (GM) as well as equa-l dimension and new-information grey model. The forecasting algorithm can estimate model parameters, meet the requirements of dynamic power load and overcome random disturbances. Example analysis shows that the forecasting error is below 3 percent. Compared with conventional theoretical methods, the proposed scheme has the characters of simple computation, high forecasting precision and good applicability.

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Liu, Han, Ying Liu, Yonglian Wang, and Changchun Pan. "Hot topics and emerging trends in tourism forecasting research: A scientometric review." Tourism Economics 25, no. 3 (November 4, 2018): 448–68. http://dx.doi.org/10.1177/1354816618810564.

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Tourism forecasting has been a focal point of tourism research over the past few decades as a result of the corresponding rapid development and expansion of the tourism industry. A bibliometric analysis, based on 543 articles retrieved from the Web of Science Core Collection database, was carried out to provide insights into hot topics as well as emerging trends in tourism forecasting research. The results show that the research outputs related to tourism forecasting have grown rapidly since 2006. The observed hot topics in tourism forecasting were to predict tourism demand via various models, including time series models, econometric models, and artificial intelligence-based methods, and to compare the forecasting accuracy of models. An emerging trend of tourism forecasting is to use methods based on data from a web-based search engine. Our study provides insights and valuable information for researchers to identify new perspectives on hot topics and research frontiers.

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Yu, Yun, Bo Yang, and Fu Jiang Ge. "Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method." Applied Mechanics and Materials 483 (December 2013): 275–79. http://dx.doi.org/10.4028/www.scientific.net/amm.483.275.

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Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

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Yu, Yun, Bo Yang, and Fu Jiang Ge. "Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method." Advanced Materials Research 875-877 (February 2014): 1858–62. http://dx.doi.org/10.4028/www.scientific.net/amr.875-877.1858.

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Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

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Jiang, Weiwei, Jiayun Luo, Miao He, and Weixi Gu. "Graph Neural Network for Traffic Forecasting: The Research Progress." ISPRS International Journal of Geo-Information 12, no. 3 (February 27, 2023): 100. http://dx.doi.org/10.3390/ijgi12030100.

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Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.

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Zema, Tomasz, and Adam Sulich. "Models of Electricity Price Forecasting: Bibliometric Research." Energies 15, no. 15 (August 3, 2022): 5642. http://dx.doi.org/10.3390/en15155642.

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Electricity Price Forecasting (EPF) influences the sale conditions in the energy sector. Proper models of electricity price prognosis can be decisive for choice between energy sources as a start point of transformation toward renewable energy sources. This article aims to present and compare various EPF models scientific publications. Adopted in this study procedure, the EPF publications models are compared into two main categories: the most popular and the most accurate. The adopted method is a bibliometric study as a variation of Systematic Literature Review (SLR) with specified automated queries supported by the VOSviewer bibliometric maps exploration. The subject of this research is the exploration of EPF models in two databases, Web of Science and Scopus, and their content comparison. As a result, the SLR research queries were classified into two groups, the most cited and most accurate models. Queries characteristics were explained, along with the graphical presentation of the results. Future promising research avenues can be dedicated to the most accurate EPF model formulation proved by statistical testing of its significance and accuracy.

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Harvey, Nigel. "Use of heuristics: Insights from forecasting research." Thinking & Reasoning 13, no. 1 (February 2007): 5–24. http://dx.doi.org/10.1080/13546780600872502.

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Liang, Xin-Zhong, Min Xu, Xing Yuan, Tiejun Ling, Hyun I. Choi, Feng Zhang, Ligang Chen, et al. "Regional Climate–Weather Research and Forecasting Model." Bulletin of the American Meteorological Society 93, no. 9 (September 1, 2012): 1363–87. http://dx.doi.org/10.1175/bams-d-11-00180.1.

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The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model. This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

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