Journal articles: 'FORECASTING TOOL'

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Veenadhari, Dr S. "Crop Advisor: A Software Tool for Forecasting Paddy Yield." Bonfring International Journal of Data Mining 6, no. 3 (July 31, 2016): 34–38. http://dx.doi.org/10.9756/bijdm.10461.

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Lindh, Thomas. "Demography as a forecasting tool." Futures 35, no. 1 (February 2003): 37–48. http://dx.doi.org/10.1016/s0016-3287(02)00049-6.

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Maksarov, Vyacheslav, and A. Khalimonenko. "Forecasting Performance of Ceramic Cutting Tool." Key Engineering Materials 736 (June 2017): 86–90. http://dx.doi.org/10.4028/www.scientific.net/kem.736.86.

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The article considers the problems of forecasting the performance of cutting tools equipped with replaceable ceramic cutting bits. It is proposed to forecast the operability of ceramic tools on the ground of dependence between its performance characteristics and the microstructural parameters of the tool material. It is proposed to determine the parameters of ceramic bits microstructure by a nondestructive testing methods based on measuring the specific electrical resistance of ceramic materials. As a result of the study we have undertaken, a relationship was detected between the performance and specific electrical resistance of ceramic cutting tools.

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Kossov, V. V. "Normalized Prices as a Forecasting Tool." Studies on Russian Economic Development 33, no. 3 (June 2022): 336–43. http://dx.doi.org/10.1134/s1075700722030066.

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&NA;. "Delphi Forecasting as a Planning Tool." Nursing Management (Springhouse) 21, no. 4 (April 1990): 18???19. http://dx.doi.org/10.1097/00006247-199004000-00006.

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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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Diez-Sierra, Javier, and Manuel del Jesus. "A rainfall analysis and forecasting tool." Environmental Modelling & Software 97 (November 2017): 243–58. http://dx.doi.org/10.1016/j.envsoft.2017.08.011.

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Jia, Jiang Ming, Yan Mei Liu, and Yun Hui Li. "Key Material Supply Forecasting Diagnostics with Dynamic Bayesian Network." Applied Mechanics and Materials 58-60 (June 2011): 1529–34. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1529.

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When supply channels varied increasingly, key material supply forecasting has become indispensable to effective operations management. Rapid technological changes and an abundance of product configurations mean that the supply for key material is frequently volatile and hard to forecast. The paper describes a key material supply forecasting diagnostics tools based on Dynamic Bayesian Network (DBN). The tool was embodied parametric description of some important factors in key material supply forecasting. Furthermore, we developed this tool to pool supply patterns of little or no supply history data. Finally, we solve this reasoning problem with stochastic simulation.

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Lawnik, Marcin, and Arkadiusz Banasik. "Delphi Method Supported by Forecasting Software." Information 11, no. 2 (January 27, 2020): 65. http://dx.doi.org/10.3390/info11020065.

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The Delphi method is one of the basic tools for forecasting values in various types of issues. It uses the knowledge of experts, which is properly aggregated (e.g., in the form of descriptive statistics measures) and returns to the previous group of experts again, thus starting the next round of forecasting. The multi-stage prediction under the Delphi method allows for better stabilization of the results, which is extremely important in the process of forecasting. Experts in the forecasting process often have access to time series forecasting software but do not necessarily use it. Therefore, it seems advisable to add to the aggregate the value obtained using forecasting software. The advantage of this approach is in saving the time and costs of obtaining a forecast. That should be understood as a smaller burden on data analysts and the value of their work. According to the above mentioned key factors, the main contribution of the article is the use of a virtual expert in the form of a computer-enhanced mathematical tool, i.e., a programming library for a forecasting time series. The chosen software tool is the Prophet library—a Facebook tool that can be used in Python or R programming languages.

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Zverev, Egor A., Pavel Tregubchak, Nikita Vakhrushev, and Stanislav Ptitsyn. "Specifications of Machine-Tool Equipment: Forecasting Techniques." Applied Mechanics and Materials 788 (August 2015): 318–24. http://dx.doi.org/10.4028/www.scientific.net/amm.788.318.

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The problems of theoretical grounds of machine tools specifications based on mathematic operational simulation are discussed in the paper. The proposed approach is based on the probability theory and mathematical statistics apparatus. It is universal and makes it possible to use automated design engineering systems at an initial development phase of the general concept of new equipment.

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Pakhomov, Andrew L., Valery F. Kalinin, Boris A. Makeev, and Alexandra V. Zueva. "APL as a tool for scientific forecasting." ACM SIGAPL APL Quote Quad 23, no. 1 (July 15, 1992): 175–82. http://dx.doi.org/10.1145/144052.144117.

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de Zeeuw-van Dalfsen, Elske, and Michael P. Poland. "Microgravity as a tool for eruption forecasting." Journal of Volcanology and Geothermal Research 442 (October 2023): 107910. http://dx.doi.org/10.1016/j.jvolgeores.2023.107910.

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KOSTYRIN, EVGENY V., and STEPAN G. DRYNKIN. "FERHULST EQUATION AS A DEMOGRAPHIC FORECASTING TOOL." Scientific Works of the Free Economic Society of Russia 240, no. 2 (2023): 407–35. http://dx.doi.org/10.38197/2072-2060-2023-240-2-407-435.

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In the scientific study, prognostic models of the population of Russia, China and Guinea were developed using the Ferhulst equation. Verification of the developed models based on an estimate of the average error of approximation of data for the countries under consideration over the past 20 years has been carried out. The results obtained were compared with the forecasts of the United Nations and conclusions were drawn about the effectiveness and applicability of the Ferhulst equation as a demographic forecasting tool.

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Gubarev, R. V., and E. I. Dzyuba. "Neuromathematics as an Effective Tool for Forecasting Social Development of Russian Regions." Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki 161, no. 2 (2019): 315–21. http://dx.doi.org/10.26907/2541-7746.2019.2.315-321.

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Bibik, Vladislav, and Elena Petrova. "Methods of Forecasting Wear Resistance of Cutting Tools Based on the Properties of their Materials." Applied Mechanics and Materials 682 (October 2014): 491–94. http://dx.doi.org/10.4028/www.scientific.net/amm.682.491.

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The author considers methods of forecasting metal-cutting tool life based on characteristics of cutting tool material. These characteristics depend on differences in numerical values of physical and chemical properties of tool material due to changes in its composition, structure, and production process variables. The described methods allow obtaining the information necessary for forecasting the tool life beyond the process of cutting, for example at the stage of cutting tool manufacturing. The author suggests using the method of registration of thermo-physical properties of the tool material as a promising forecasting technique.

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Slonim, Ori. "National intelligence: A tool for political forecasting and the forecasting of rare events." Technological Forecasting and Social Change 128 (March 2018): 245–51. http://dx.doi.org/10.1016/j.techfore.2017.04.019.

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Dietmair, A., and A. Verl. "ENERGY CONSUMPTION FORECASTING AND OPTIMISATION FOR TOOL MACHINES." MM Science Journal 2009, no. 01 (March 18, 2009): 63–67. http://dx.doi.org/10.17973/mmsj.2009_03_20090305.

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Smagin, A. A., and V. S. Poletaev. "A software tool for information security threats forecasting." Infokommunikacionnye tehnologii 16, no. 2 (2018): 192–98. http://dx.doi.org/10.18469/ikt.2018.16.2.06.

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Neiman, P. J., A. B. White, F. M. Ralph, D. J. Gottas, and S. I. Gutman. "A water vapour flux tool for precipitation forecasting." Proceedings of the Institution of Civil Engineers - Water Management 162, no. 2 (April 2009): 83–94. http://dx.doi.org/10.1680/wama.2009.162.2.83.

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Bellaire, S., J. B. Jamieson, and C. Fierz. "pSNOWPACK: a forecasting tool for avalanche warning services." Cryosphere Discussions 5, no. 4 (August 23, 2011): 2253–78. http://dx.doi.org/10.5194/tcd-5-2253-2011.

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Abstract. Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km2) and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. We assess the capability of this model chain (pSNOWPACK) to forecast three key factors of snow cover instability at a single point: new snow amounts, surface hoar formation and crust formation. The output of GEM15 was compared to meteorological data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth, new snow events and amounts were well modelled. Relevant critical layers, i.e. melt-freeze crusts and surface hoar layers were reproduced. Overall, the model chain pSNOWPACK shows promising potential as a forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere.

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Raybould, Caroline. "Trends forecasting as a tool for sustainable education." Fashion, Style & Popular Culture 00, no. 00 (February 18, 2021): 1–14. http://dx.doi.org/10.1386/fspc_00058_1.

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The fashion and textile industry is under increasing scrutiny because of its unethical and unsustainable practices. It is clear there needs to be systemic change towards a more ecological future. One way to achieve this is through education, by equipping students with strategies and skills and by nurturing sustainable mindsets. How can we create the next generation of fashion professionals who can help bring the change that is much needed? Having taught sustainability within various modules on a fashion business degree in the United Kingdom, it was observed that a significant number of students engaged at a deeper level with sustainable thinking when learning trends forecasting research. A pilot study was trialled when teaching a short course in India with a small group of interdisciplinary design students and a questionnaire was conducted after the workshop. This article presents findings and reflections of this cross-cultural experience, with suggestions for future projects and educational approaches.

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Benhamida, Fatima Zohra, Ouahiba Kaddouri, Tahar Ouhrouche, Mohammed Benaichouche, Diego Casado-Mansilla, and Diego Lopez-de-Ipina. "Demand Forecasting Tool For Inventory Control Smart Systems." Journal of Communications Software and Systems 17, no. 2 (2021): 185–96. http://dx.doi.org/10.24138/jcomss-2021-0068.

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Chang, Tung-Meng, and Chun-Hsiung Lan. "A modularized forecasting tool for ordering/inventory cost." Journal of Statistics and Management Systems 4, no. 3 (January 2001): 239–54. http://dx.doi.org/10.1080/09720510.2001.10701041.

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Plotnick, Robert D., and Russell M. Lidman. "Forecasting Welfare Caseloads: A Tool to Improve Budgeting." Public Budgeting & Finance 7, no. 3 (January 1987): 70–81. http://dx.doi.org/10.1111/1540-5850.d01-233.

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Nicolle, Pierre, François Besson, Olivier Delaigue, Pierre Etchevers, Didier François, Matthieu Le Lay, Charles Perrin, et al. "PREMHYCE: An operational tool for low-flow forecasting." Proceedings of the International Association of Hydrological Sciences 383 (September 16, 2020): 381–89. http://dx.doi.org/10.5194/piahs-383-381-2020.

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Abstract. In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower, etc.) and they can be strongly affected by water shortages. Therefore, there is a need for the early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low flows in the context of climate change. Several French institutions (Inrae, BRGM, Météo-France, EDF and Lorraine University) have been collaborating over the last years to develop an operational tool for low-flow forecasting, called PREMHYCE. It was tested in real time on 70 catchments in continental France in 2017, and on 48 additional catchments in 2018. PREMHYCE includes five hydrological models: one uncalibrated physically-based model and four storage-type models of various complexity, which are calibrated on gauged catchments. The models assimilate flow observations or implement post-processing techniques. Low-flow forecasts can be issued up to 90 d ahead, based on ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios. These climatic data (precipitation, potential evapotranspiration and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis over the 1958–2017 period, which includes a wide range of conditions. The tool provides numerical and graphical outputs, including the forecasted ranges of low flows, and the probability to be under low-flow warning thresholds provided by the users. Outputs from the different hydrological models can be combined through a simple multi-model approach to improve the robustness of forecasts. Results are illustrated for the Ill River at Didenheim (northeastern France) where the 2017 low-flow period was particularly severe and for which PREMHYCE provided useful forecasts.

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Korpela, Jukka, and Markku Tuominen. "Inventory forecasting with a multiple criteria decision tool." International Journal of Production Economics 45, no. 1-3 (August 1996): 159–68. http://dx.doi.org/10.1016/0925-5273(95)00136-0.

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Valencia-Barrera, Rosa, Paul Comtois, and Delia Fernández-González. "Bioclimatic indices as a tool in pollen forecasting." International Journal of Biometeorology 46, no. 4 (September 1, 2002): 171–75. http://dx.doi.org/10.1007/s00484-002-0138-y.

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Redmon, Robert J., David Anderson, Ron Caton, and Terence Bullett. "A Forecasting Ionospheric Real-time Scintillation Tool (FIRST)." Space Weather 8, no. 12 (December 2010): n/a. http://dx.doi.org/10.1029/2010sw000582.

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Dhakal, Chuda Prasad, and Hari Bhakta Shahi. "Naïve Forecasting: A Tool to Compare Forecast Models." Nepal Journal of Mathematical Sciences 4, no. 1 (April 4, 2023): 47–50. http://dx.doi.org/10.3126/njmathsci.v4i1.53156.

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In this study, a freshly fitted forecast model is put up against a standard procedure for comparison. But first, the essay makes a distinction between the confusing notion of a prediction model's accuracy measure and a comparison of forecast models in terms of gauging their relative and absolute accuracy measures in various scenarios. A forecast model's accuracy measure by itself does not give a complete picture of how much better a newly fitted model is than other benchmark models built from the same dataset. This article illustrates the comparison of a multiple regression model as a novel fit with the naive forecasting methodology, a well-known benchmark in the forecasting area, using cross-validation techniques. The performance of the forecast models was assessed using two generally used accuracy measures, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). It was discovered that the multiple regression model performs better than the naive technique in both MAE and MAPE. This meant the multiple regression model was a worthy fit. In summary, it is crucial to compare a newly developed forecast model with benchmark models to evaluate its performance accurately. This process allows for the identification of the most suitable forecasting method for a specific context and promotes the development of improved techniques for comparing forecast models in the future.

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Bonde, Hans, and Hans-Henrik Hvolby. "The demand planning process." Journal on Chain and Network Science 5, no. 2 (December 1, 2005): 73–84. http://dx.doi.org/10.3920/jcns2005.x057.

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In this paper, demand planning is discussed from a process point of view. Demand planning is not only forecasting but goes beyond, as it combines quantitative forecasting with a causal forecasting approach to plan demand by changing factors within pricing, marketing or selling. A four-phase demand planning process model is introduced, which consists of modeling, forecasting, demand planning and supply planning. The core of the process is a demand planning tool, which allows the combination of quantitative, causal and judgmental forecasting. Finally, some thoughts are given on how SMEs can develop their current forecasting practice when implementing demand planning as a strategic and tactical tool.

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Hidalgo, I. G., P. S. F. Barbosa, A. L. Francato, I. Luna, P. B. Correia, and P. S. M. Pedro. "Management of inflow forecasting studies." Water Practice and Technology 10, no. 2 (June 1, 2015): 402–8. http://dx.doi.org/10.2166/wpt.2015.050.

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In hydroelectric systems, water inflow is important to coordinate a cascade and define the energy price. This paper presents a method for managing inflow forecasting studies with a specific module for advanced assessment. The main goal is to provide a structure that facilitates the analysis of water inflow prediction models. A case study has been applied to five mathematical models based on linear regression, artificial neural networks, and hydrologic simulation. These models present daily and monthly inflow forecasts for a set of hydroelectric plants and monitoring stations. The benefits of the proposed method are analyzed in four situations: water inflow prediction, performance evaluation of a specific model, research tool for inflow forecasting, and comparison tool for distinct models. The results show that implementation of the proposed method provides a useful tool for managing inflow forecasting studies and analyzing models. Therefore, it can assist researchers and engineering professionals alike by improving the quality of water inflow predictions.

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Alkhayat, Ghadah, Syed Hamid Hasan, and Rashid Mehmood. "SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting." Energies 15, no. 18 (September 12, 2022): 6659. http://dx.doi.org/10.3390/en15186659.

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Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (LSTM-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The LSTM-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution, different input features, different optimizations of the same models, or other user preferences.

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Hasan, M. Babul, M. Asadujjaman, and M. Hasibul Haque. "An Integrated Forecasting Technique with Modified Weight Measurement." Dhaka University Journal of Science 71, no. 1 (May 29, 2023): 36–41. http://dx.doi.org/10.3329/dujs.v71i1.65270.

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Forecasting has long been part of our life since early of the history of human being. In the middle of 20th century forecasting becomes a part of every business and financial sectors. Nowadays every successful firm has to make its own forecasts with an acceptable error as there is no chance of zero error. The situation becomes more complicated if the observed data is more diverted from the existing pattern. In such situation it becomes more difficult to fit it into a suitable forecasting model.Then it requires to combine several forecasts to reach a better forecast.In this paper, we willdevelop a sophisticated forecasting technique bycombining the weighted average method with Linear Programming (LP) model by developing an alternative technique to calculate the weights. We will carry out our analysis by using Microsoft Excel, statistical data analysis tool R and MATHEMATICA. We will demonstrate our model by numerical examples. Dhaka Univ. J. Sci. 71(1): 36-41, 2023 (Jan)

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Purohit, Shivani K., and Ashish K. Sharma. "Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment." International Journal of Business Analytics 4, no. 1 (January 2017): 56–86. http://dx.doi.org/10.4018/ijban.2017010104.

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Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to improve competitiveness and increase customer satisfaction. The purpose can be served by using Data Mining techniques of forecasting. With the pool of forecasting techniques available, it is important to evaluate a suitable one for more effective results. To this end, the paper presents a novel software tool to efficiently forecast CRs in QFD. The tool allows for forecasting using various data mining based time series analysis techniques that strongly assists in doing comparative analysis and evaluating out the most apt technique for forecasting of CRs. The tool is developed using VB.Net and MS-Access. Finally, an example is presented to demonstrate the practicability of proposed software tool.

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Kmiecik, Mariusz. "Automation of warehouse resource planning process by using a cloud demand forecasting tool." Scientific Papers of Silesian University of Technology. Organization and Management Series 2023, no. 166 (2023): 391–409. http://dx.doi.org/10.29119/1641-3466.2022.166.26.

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Purpose: Research paper is an extension of the concept connected with demand forecasting function acquisition by logistics operators in the whole distribution network (concept is considered among others in the following papers: (Kmiecik, 2021a, 2021b, 2020). The concept of centralized forecasting assumes that a logistics operator, whose attributes coincide with the features selected through analysis of forecasting-able entities in the distribution networks and flagship enterprises, is able to take over the forecasting function for the whole distribution network. Paper, which is based on the mentioned concept, shows one of the first stages of its implementation. This stage is the implementation of the forecasting tool in the logistics operator's actions to support his operational activities. Currently, the logistics operator doesn't conduct the forecasting activity, but there are attempts aimed at implementation of forecasting tool and increasing the offered services' added value level. Operational activity, which will be first to be supported, is the process of planning the warehouse resources. Mentioned resources concern the human and internal transportation resources, which are needed for fulfilling the processes connected with SKU (Stock Keeping Units) releasing from the warehouse. The main paper purpose to examine the concept the of automated cloud-based resource planning process at the selected 3PL entity which provides logistics services to wide range of manufacturers in the distribution network using the computer simulation model with comparison with the current resource planning process. Design/methodology/approach: Following research paper based on analysis the survey results and analyzing the simulation results. In the survey were tested the warehouse managers which are responsible for resource planning process. The analysis provides the general requirements of managers about resource planning process supported by automated cloud-based demand forecasting solution and information about expected forecasting tool accuracy. In the paper there were also created two simulation models based on BPMN 2.0 standard. First model reflects the current shape of resource planning process and was created to compare to the second, improved model. Improved model includes the examining of automated cloud-based resource planning solution. Findings: The main expectations of 3PL operational managers about usage the demand forecasting tool is to support of warehouse resource planning. They also state that the expected accuracy of such a tool is the weekly MAPE not greater than 5%. The main benefits of proposed solution are the time decreasing, increasing the level of automation, showing the main areas when the agile point of view should be implemented and show the perspective of resource possibility of usage in the different activities (beside the resource planning process). Originality/value: Automation and fully cloud integration will allow to reduce the process time more than 60% (in total and in average one process time). There are also some disadvantages of proposed solution which could be reduced by using other trends connected with Industry 4.0 development and by developing the collaborative strategies of the particular nodes of distribution networks. Keywords: logistics operator; distribution network; warehouse management; demand forecasting; BPMN 2.0 model. Category of the paper: research paper.

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Solovev, Bogdan, and Giorgi Gamisonia. "WIND POWER PREDICTION METHODS FOR SHELF WIND POWER PLANTS." Electrical and data processing facilities and systems 18, no. 3-4 (2022): 108–20. http://dx.doi.org/10.17122/1999-5458-2022-18-3-4-108-120.

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Relevance Wind energy forecasting is an opportunity to evaluate the production possibilities of a wind farm in the short term. Production often refers to the available capacity of the wind farm in question. For example, to date, the installed wind power in Russia has reached 20 GW. Direct transmission operators use existing tools to forecast wind production up to 48 hours. Forecasting tools help optimize power system management. This article discusses the abundance of relevant forecasting methods in the field of wind energy, evaluates their effectiveness and value for the most effective control of wind energy. Particular attention is paid to the ongoing development of wind energy forecasting models to meet the specifics of shelf. Aim of research Conduct a comparative analysis of existing forecasting methods in the field of wind energy under general given conditions, choose the best method for a particular case. Research methods To solve the problem, the authors conducted a comparative analysis of the popular, currently existing methods for forecasting wind farms, comparing their applicability with the specification of the area of use. Results In the course of the study, modern wind energy forecasting tools were analyzed, a comparative analysis was carried out, and conclusions were drawn about the applicability of each of the methods. Keywords: wind energy, short-term forecasting, shelf, optimization, efficiency, model, tool, control, mathematical model, forecast error level

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Zhang, Haipeng, and Hua Luo. "An Advanced Hybrid Forecasting System for Wind Speed Point Forecasting and Interval Forecasting." Complexity 2020 (November 21, 2020): 1–16. http://dx.doi.org/10.1155/2020/7854286.

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Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing strategy, a novel optimization model, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming.

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Das, Himangshu S., Hoonshin Jung, Bruce Ebersole, Ty Wamsley, and Robert W. Whalin. "AN EFFICIENT STORM SURGE FORECASTING TOOL FOR COASTAL MISSISSIPPI." Coastal Engineering Proceedings 1, no. 32 (February 1, 2011): 21. http://dx.doi.org/10.9753/icce.v32.currents.21.

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Coupled storm surge simulations with fine resolution have become a reality due to the rapid development of computer power and advancement in the integration of the simulation models. However, the real-time application of such robust simulations is often constrained by the availability of time and computational resources. In this study, an alternative, Storm Surge Forecasting Tool (SSFT) has been developed to forecast storm surge in Coastal Mississippi. The algorithm of SSFT uses a weight based Storm Similarity Index (SSI) that is defined by current hurricane position Central Pressure (CP), Pressure Scale Radius (Rmax) along with hurricane track, landfall location, storm forward speed, and forecasted storm track published by the National Hurricane Centre (NHC) and correlated with the characteristics of synthetic storms within the underlying database. Based on the values of SSI (scales from 0 to 1), the SSFT identifies a group of storms that much as close as possible with the characteristics of the approaching hurricanes and then display high resolution simulation results (e.g., maximum surge elevation and hydrographs). The SSFT model operates in two different modes:1) Hindcasting mode and 2) Forecasting mode. The SSFT GUI was tested in both modes and we found that the method is very promising. Using this tool and approach as a decision aide, the emergency personnel can quickly forecast local storm surge along the coast of Mississippi. This will allow them to make quantitative and objective decisions by evaluating “what-if-scenarios” starting two to three days ahead of the landfall.

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Cockshott, Anne. "Reservoir computing, a machine learning tool for robust forecasting." Scilight 2021, no. 51 (December 17, 2021): 511107. http://dx.doi.org/10.1063/10.0009049.

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Kmiecik, Mariusz, and Hawre Zangana. "Supporting of manufacturing system based on demand forecasting tool." Logforum 18, no. 1 (March 30, 2022): 35–50. http://dx.doi.org/10.17270/j.log.2022.637.

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Hoffman, Eric G. "Surface Potential Temperature as an Analysis and Forecasting Tool." Meteorological Monographs 55 (November 1, 2008): 163–82. http://dx.doi.org/10.1175/0065-9401-33.55.163.

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Abstract In the last decade, Fred Sanders was often critical of current surface analysis techniques. This led to his promoting the use of surface potential temperatures to distinguish between fronts, baroclinic troughs, and non-frontal baroclinic zones, and to the development of a climatology of surface baroclinic zones. In this paper, criticisms of current surface analysis techniques and the usefulness of surface potential temperature analyses are discussed. Case examples are used to compare potential temperature analyses and current National Centers for Environmental Prediction analyses. The 1-yr climatology of Sanders and Hoffman is reconstructed using a composite technique. Annual and seasonal mean potential temperature analyses over the continental United States, southern Canada, northern Mexico, and adjacent coastal waters are presented. In addition, gridpoint frequencies of moderate and strong potential temperature gradients are calculated. The results of the mean potential temperature analyses show that moderate and strong surface baroclinic zones are favored along the coastlines and the slopes of the North American cordillera. Additional subsynoptic details, not found in Sanders and Hoffman, are identified. The availability of the composite results allows for the calculation of potential temperature gradient anomalies. It is shown that these anomalies can be used to identify significant frontal baroclinic zones that are associated with weak potential temperature gradients. Together the results and reviews in this paper show that surface potential temperature analyses are a valuable forecasting and analysis tool allowing analysts to distinguish and identify fronts, baroclinic troughs, and nonfrontal baroclinic zones.

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Al-Yahyai, Sultan, and Yassine Charabi. "Trajectory Calculation as Forecasting Support Tool for Dust Storms." Advances in Meteorology 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/698359.

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In arid and semiarid regions, dust storms are common during windy seasons. Strong wind can blow loose sand from the dry surface. The rising sand and dust is then transported to other places depending on the wind conditions (speed and direction) at different levels of the atmosphere. Considering dust as a moving object in space and time, trajectory calculation then can be used to determine the path it will follow. Trajectory calculation is used as a forecast supporting tool for both operational and research activities. Predefined dust sources can be identified and the trajectories can be precalculated from the Numerical Weather Prediction (NWP) forecast. In case of long distance transported dust, the tool should allow the operational forecaster to perform online trajectory calculation. This paper presents a case study for using trajectory calculation based on NWP models as a forecast supporting tool in Oman Meteorological Service during some dust storm events. Case study validation results showed a good agreement between the calculated trajectories and the real transport path of the dust storms and hence trajectory calculation can be used at operational centers for warning purposes.

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Tsang, Edward, Paul Yung, and Jin Li. "EDDIE-Automation, a decision support tool for financial forecasting." Decision Support Systems 37, no. 4 (September 2004): 559–65. http://dx.doi.org/10.1016/s0167-9236(03)00087-3.

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Smith, William L., Gary S. Wade, and Harold M. Woolf. "Combined Atmospheric Sounding/Cloud Imagery—A New Forecasting Tool." Bulletin of the American Meteorological Society 66, no. 2 (February 1985): 138–41. http://dx.doi.org/10.1175/1520-0477(1985)066<0138:casinf>2.0.co;2.

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Taeb, Peyman, and Robert J. Weaver. "An operational coastal forecasting tool for performing ensemble modeling." Estuarine, Coastal and Shelf Science 217 (February 2019): 237–49. http://dx.doi.org/10.1016/j.ecss.2018.09.020.

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Dai, Wensheng, Jui-Yu Wu, and Chi-Jie Lu. "Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/438132.

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Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

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Otero-Casal, Carlos, Platon Patlakas, Miguel A. Prósper, George Galanis, and Gonzalo Miguez-Macho. "Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter." Energies 12, no. 16 (August 8, 2019): 3050. http://dx.doi.org/10.3390/en12163050.

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Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets.

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Sahed, Abdelkader. "Using a GMDH-type neural network and ARIMA model to forecasting GDP in Algeria during the period of 1990-2019." American Journal of Economics and Business Management 3, no. 4 (October 14, 2020): 37–47. http://dx.doi.org/10.31150/ajebm.v3i4.200.

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Forecasting is a method to predict the future using data and the last information as a tool assists in planning to be effective. GMDH-Type (Group Method of Data Handling) artificial neural network (ANN) and Box-Jenkins method are among the know methods for time series forecasting of mathematical modeling. in the present study GMDH-type neural network and ARIMA method has been used to forecasted GDP in Algeria during the period 1990 to2019 (Time series of quarterly observations on Gross Domestic Product (GDP) is used). Root mean square error (RMSE) was used as performance indices to test the accuracy of the forecast. The empirical results for both models showed that the GMDH model is a powerful tool in forecasting GDP and it provides a promising technique in time series forecasting methods.

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Vigdorowitsch, Michael. "Differential Model of Customer Loyalty Evolution as Loyalty Forecasting Tool." International Journal of Services and Operations Management 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijsom.2022.10049825.

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Graf, B., H. U. Höpli, H. Höhn, and PH Blaise. "SOPRA: A FORECASTING TOOL FOR INSECT PESTS IN APPLE ORCHARDS." Acta Horticulturae, no. 584 (July 2002): 207–14. http://dx.doi.org/10.17660/actahortic.2002.584.25.

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Journal articles on the topic 'FORECASTING TOOL'

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