Bibliographies thématiques / FORECASTING TOOL

Littérature scientifique sur le sujet « FORECASTING TOOL »

Auteur : Grafiati
Publié le 26 octobre 2023

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Articles de revues sur le sujet "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 (31 juillet 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 (février 2003) : 37–48. http://dx.doi.org/10.1016/s0016-3287(02)00049-6.

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Maksarov, Vyacheslav, et A. Khalimonenko. « Forecasting Performance of Ceramic Cutting Tool ». Key Engineering Materials 736 (juin 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 (juin 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 (avril 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 (octobre 2003) : n/a. http://dx.doi.org/10.1029/2003sw000029.

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

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Jia, Jiang Ming, Yan Mei Liu et Yun Hui Li. « Key Material Supply Forecasting Diagnostics with Dynamic Bayesian Network ». Applied Mechanics and Materials 58-60 (juin 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, et Arkadiusz Banasik. « Delphi Method Supported by Forecasting Software ». Information 11, no 2 (27 janvier 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 et Stanislav Ptitsyn. « Specifications of Machine-Tool Equipment : Forecasting Techniques ». Applied Mechanics and Materials 788 (août 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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Thèses sur le sujet "FORECASTING TOOL"

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Ljungström, Erica. « ISAT : Interactive Scenario Analysis Tool for financial forecasting ». Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177128.

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Målet för denna studie har varit att skapa en första version för ett verktyg i vilket analytiker inom finans kan skapa sina långsiktiga scenarion och väga olika rikser samt möjligheter mot varandra.Idén till ett sådant verktyg har funnits inom företaget i flera år, men de tidigare idéerna har varit för specifika för att kunna användas. Detta har främst berott på tidsbrist samt att de som utvecklat dem inte har haft tillgång till en bra utvecklingsmiljö.De enda begränsningarna för verktyget har varit 1) ”Det behöer kunna visa inverkan av manipuleringar”, 2) ”det behöer finnas såmycket funktionalitet som möjligt, utan att det finns knappar överallt” och 3) ”det få inte ädra nåot av input datan”.Eftersom dessa är abstrakta specificationer behövdes mock-ups, observationer och användbarhetstest för att kunna skapa ett verktyg som förenklar de mest använda manipulationerna. Verktyget måste också låta användaren enkelt ticka i och ur sina manipulationer så att dessa inte behöver göras om varje gång användaren vill testa ett nytt utfall.Observationerna och testerna har visat att användarna jobbar på olika sätt, och därför behövde verktyget vara flexibelt. Detta innebar också att det behövdes både generella samt specifika manipulationer. De visade också att verktyget behövde delas in i två delar, en för att skapa rapporter och en för att visa rapporten. Detta då rapporteringsprocessen ej får ändras.Fokus för denna studie har varit MDI, Människa-datorinteraktion. Detta innebär att den färdiga produkten bör vara intuitiv och väldigt enkel att lära sig att använda för användarna. Detta kan vara svårt då användarna arbetar på mycket olika sätt.Den färdiga produkten för denna studie har lyckats klara alla de mål som satts upp. En mock-up som gjorde användarna nyfikna på programmet, skapades i Java. Det bestämdes därmed att detta var det programmeringsspråk som skulle användas. Ett användargränssnitt som var enkel, men samtidigt hade en komplex funktionalitet lades till.Detta gjorde att användarna frågade sig själva ”Kan det verkligen vara såhä enkelt?” samt ”Varfö har vi inte gjort detta innan?”. Tillslut skapades en fungerande produkt som var både enkel att använda samt att den gjorde många av de enkla beräkningarna åt analytikern.Den enda del som ej blivit fullt implementerad innan slutet för denna studie är mallen för de Excel rapporter som ska skapas av verktyget. Denna del av verktyget utformades av en ekonom som vet vilka grafer som kan vara intressanta att ta med i en rapport. Nu, när verktyget genererar en rapport, genereras endast de grafer som syns i verktyget, totalen för scenariona (uppdelade i olika kategorier) samt alla de manipuleringsrader som skapats för de tre olika scenariona.
The goal with this study has been to create a first version for a tool in which financial analysts can create their long-term scenarios and weigh different risks and opportunities against each other.The idea to such a tool has been around for years within the company, but the earlier ideas were too specific to be usable. This has mainly been due to the lack of time and available tools to realize the ideas.The only restrictions for the tool have been 1) “It needs to show the impact of manipulations”, 2) “it needs as much functionality as possible without having buttons all over it” and 3) “it should not alter any of the input data”.Because these are quite abstract specifications, mock-ups, observations and usability tests have been used to create a tool that simplifies the most used manipulations and enables the user to tick in and out their manipulations so that the manipulation does not have to be redone every time the user wants to test a new outcome.The observations and tests have shown that the users work very differently from each other, and so, the tool needed to be very flexible. This meant that there needed to be both general and specific manipulations which are based on general formulas. It also showed that the tool needed to be split into two parts, one for creating and one for showing reports, because the reporting process should not be altered.The focus of this study has been HCI, Human Computer Interaction. This means that the finished product should be intuitive and also easy to learn how to operate by the users which could be difficult when the users do work in different ways. The resulting product of this study has reached all of the goals. A mock-up that got the users interested in the program was produced in Java, which decided the programming language. A GUI that was simple, yet had complex functionality was added. It made users ask themselves “Could it really be this easy?” and “Why have we not done this before?”. And, at last, a working product were produced, that was both simple to operate and at the same time did a lot of the calculations for the analyst.The only part of the product that has not been fully implemented before the endof this study is the template in which the Excel Report is supposed to be generated. This part of the tool was taken care of by an economist that knew which graphs that could be interesting to create a report of. Now, the tool generates a report with only the graphs that are shown in the tool, the totals for the scenarios (split into different categories) and all of the adjustment rows for the three scenarios.
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Seemann, Thomas. « Prediction markets as forecasting tool in decision processes ». Berlin Pro Business, 2008. http://d-nb.info/993592570/04.

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Li, Jin. « FGP : a genetic programming based tool for financial forecasting ». Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343550.

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Yaufman, Mariah B. « A Discontinuous Galerkin-based Forecasting Tool for the Ohio River ». The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469035735.

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Choi, Ji Won. « Forecasting potential project risks through leading indicators to project outcome ». Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/5973.

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During project execution, the status of the project is periodically evaluated, using traditional methods or standard practices. However, these traditional methods or standard practices may not adequately identify certain issues, such as lack of sufficient identification of warning signs that predict potential project failure. Current methods may lack the ability to provide real time indications of emerging problems that impact project outcomes in a timely manner. To address this problem, the Construction Industry Institute (CII) formed a research team to develop a new tool that can forecast the potential risk of not meeting specific project outcomes based on assessing leading indicators. Thus, the leading indicators were identified and then the new tool was developed and validated. A screening process was conducted through industry surveys after identifying potential leading indicators. Each time, industry professionals were asked to evaluate the negative impact of leading indicators on project outcomes that were identified to measure the impact of leading indicators on project health. Through this process, forty-three leading indicators were acquired finally. Using descriptive statistics, the amount of negative impact of each leading indicator on project outcomes was identified after the analysis of the survey results. Based on these impacts, the tool development was initiated. The tool concept is that no indication of problems based on assessing leading indicators results in the tool output score high. To comply with this concept, specific weights were assigned to each leading indicator to reflect the impact on each project outcome. By this procedure, the Project Health Indicator (PHI) tool was developed. The validation process of the PHI tool was conducted using completed projects and finally negative correlation was observed between project outcomes and health scores generated by the PHI tool.
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BRITO, VICTOR BARBOZA. « FUZZYFUTURE : TIME SERIES FORECASTING TOOL BASED ON FUZZY-GENETIC HYBRID SYSTEM ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18536@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
A previsão de séries temporais está presente em diversas áreas como os setores elétrico, financeiro, a economia e o industrial. Em todas essas áreas, as previsões são fundamentais para a tomada de decisões no curto, médio e longo prazo. Certamente, as técnicas estatísticas são as mais utilizadas em problemas de previsão de séries, principalmente por apresentarem um maior grau de interpretabilidade, garantido pelos modelos matemáticos gerados. No entanto, técnicas de inteligência computacional têm sido cada vez mais aplicadas em previsão de séries temporais no meio acadêmico, com destaque para as Redes Neurais Artificiais (RNA) e os Sistemas de Inferência Fuzzy (FIS). Muitos são os casos de sucesso de aplicação de RNAs, porém os sistemas desenvolvidos são do tipo caixa preta, inviabilizando uma melhor compreensão do modelo final de previsão. Já os FIS são interpretáveis, entretanto sua aplicação é comprometida pela dependência de criação de regras por especialistas e pela dificuldade em ajustar os diversos parâmetros como o número e formato de conjuntos e o tamanho da janela. Além disso, a falta de pessoas com o conhecimento necessário para o desenvolvimento e utilização de modelos baseados nessas técnicas também contribui para que estejam pouco presentes na rotina de planejamento e tomada de decisão na maioria das organizações. Este trabalho tem como objetivo desenvolver uma ferramenta computacional capaz de realizar previsões de séries temporais, baseada na teoria de Sistemas de Inferência Fuzzy, em conjunto com a otimização de parâmetros por Algoritmos Genéticos, oferecendo uma interface gráfica intuitiva e amigável.
The time series forecasting is present in several areas such as electrical, financial, economy and industry. In all these areas, the forecasts are critical to decision making in the short, medium and long term. Certainly, statistical techniques are most often used in time series forecasting problems, mainly because of a greater degree of interpretability, guaranteed by the mathematical models generated. However, computational intelligence techniques have been increasingly applied in time series forecasting in academic research, with emphasis on Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS). There are many cases of successful application of ANNs, but the systems developed are black box, not allowing a better understanding of the final prediction. On the other hand the FIS are interpretable, but its application is compromised by reliance on rule-making by experts and by the difficulty in adjusting the various parameters as the number and shape of fuzzy sets and the window size. Moreover, the lack of people with the knowledge necessary for the development and use of models based on these techniques also restricts their application in the routine planning and decision making in most organizations. This work aims to develop a computational tool able to make forecasts of time series, based on the theory of Fuzzy Inference Systems, in conjunction with the optimization of parameters by Genetic Algorithms, providing an intuitive and friendly graphical user interface.
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Page, Alison L. 1971. « Forecasting mix-sensitive semiconductor fabrication tool set requirements under demand uncertainty ». Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/84517.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2001.
Includes bibliographical references (leaves 74-75).
by Alison L. Page.
S.M.
M.B.A.
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Watkiss, Brendon Miles. « The SLEUTH urban growth model as forecasting and decision-making tool ». Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/1654.

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Thesis (MSc (Geography and Environmental Studies))--Stellenbosch University, 2008.
Accelerating urban growth places increasing pressure not only on the efficiency of infrastructure and service provision, but also on the natural environment. City managers are delegated the task of identifying problem areas that arise from this phenomenon and planning the strategies with which to alleviate them. It is with this in mind that the research investigates the implementation of an urban growth model, SLEUTH, as a support tool in the planning and decision making process. These investigations are carried out on historical urban data for the region falling under the control of the Cape Metropolitan Authority. The primary aim of the research was to simulate future urban expansion of Cape Town based on past growth patterns by making use of cellular automata methodology in the SLEUTH modeling platform. The following objectives were explored, namely to: a) determine the impact of urbanization on the study area, b) identify strategies for managing urban growth from literature, c) apply cellular automata as a modeling tool and decision-making aid, d) formulate an urban growth policy based on strategies from literature, and e) justify SLEUTH as the desired modeling framework from literature. An extensive data base for the study area was acquired from the product of a joint initiative between the private and public sector, called “Urban Monitoring”. The data base included: a) five historical urban extent images (1977, 1988, 1993, 1996 and 1998); b) an official urban buffer zone or ‘urban edge’, c) a Metropolitan Open Space System (MOSS) database, d) two road networks, and d) a Digital Elevation Model (DEM). Each dataset was converted to raster format in ArcEdit and finally .gif images were created of each data layer for compliance with SLEUTH requirements. SLEUTH processed this historic data to calibrate the growth variables for best fit of observed historic growth. An urban growth forecast was run based on the calibration parameters. Findings suggest SLEUTH can be applied successfully and produce realistic projection of urban expansion. A comparison between modelled and real urban area revealed 76% model accuracy. The research then attempts to mimic urban growth policy in the modeling environment, with mixed results.
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Palmer, Jeffrey M. « Incorporating ensemble-based probabilistic forecasts into a campaign simulation in the Weather Impact Assessment Tool (WIAT) ». Thesis, Monterey, California : Naval Postgraduate School, 2010. http://edocs.nps.edu/npspubs/scholarly/theses/2010/Jun/10Jun%5FPalmer.pdf.

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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, June 2010.
Thesis Advisor: Stone, Rebecca. ; Second Reader: Durkee, Philip. "June 2010." Description based on title screen as viewed on July 16, 2010. Author(s) subject terms: Stochastic forecasting, probabilistic forecasting, operational simulation. Includes bibliographical references (p. 115-116). Also available in print.
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Bartholomew, Nathan. « Accurately predicting visitation as a strategic tool for management of a public park ». Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35445.

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Master of Agribusiness
Department of Agricultural Economics
Nathan P. Hendricks
Public parks can provide considerable value to the population that visit them, for the community around them and the local economy. A well designed public park can attract growth in tourism, stimulate a habitat for wildlife, contribute to personal health and wellness, improve the aesthetics of an area and stimulate economic growth. Managing and operating a public park entails many complex issues such as designing an attractive green space, implementing and maintaining the park, attracting and managing visitors and obtaining financial support. Public parks need to identify factors that influence park visitation in order to more effectively manage park visitorship.. This thesis examines park visitation analyzing data of park users of The High Line in New York City to develop a model to more accurately predict visitation. The thesis focuses on the critical social and climatic variables that attract visitors to spend time in the High Line park. Understanding these factors will allow park management the ability to create a strategic plan for managing a public space that best serves its visitors and the community. More specifically, a strategic plan helps to determine who the visitors are and what activities they enjoy in the park. In conceptualizing a solution, High Line can put into practice what its visitors want to see offered in the park and which of its programming needs improvement to attract more visitors. Meeting the needs of park visitors will create a better experience for the customers and a better management strategy for operations. A multivariate regression analysis was used to establish the relationship between High Line visitation and the climatic and social variables. The climatic variables of daily average temperature and precipitation. The social variables of day of the week and season of the year were added to the structural model as dummies. A time trend variable characterized as time in years was added to the model to show any yearly change in visitation to the park. This method has been widely applied to a number of studies testing the relationship of climatic and social variables to park visitation (Micah, Scotter and Fenech 2016). The results of this regression analysis show that the social variables of day of the week and season and the climatic variables of average temperature and precipitation had a significant affect on park visitation. The model developed can be used to forecast park visitation, quantifying the many variables that influence park visitation.
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Livres sur le sujet "FORECASTING TOOL"

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Inc, Savant, et National Association of State Units on Aging., dir. FOCUS, forecasting tool screen documentation. [Washington, D.C.?] : Savant, Incorporated, Natl. Association of State Units on Aging, 1987.

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Outwater, Maren. Developing a Method Selection Tool for Travel Forecasting. Washington, D.C. : Transportation Research Board, 2017. http://dx.doi.org/10.17226/24931.

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Godet, Michel. Futures studies : A tool-box for problem solving. Paris : GERPA/Futuribles, 1991.

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B. R. S. B. Basnayake. Seasonal weather forecasting in Bangladesh using Climate Predictability Tool (CPT). Dhaka : SAARC Meteorological Research Centre, 2010.

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Bengoechea, Pilar. A useful tool to identify recessions in the Euro-area. Brussels : European Commission, Directorate-General for Economic and Financial Affairs, 2004.

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Basnayake, B. R. S. B. Forecasting of seasonal and monthly rainfall in Sri Lanka using Climate Predictability Tool (CPT). Dhaka : SAARC Meteorological Research Centre, 2009.

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B. R. S. B. Basnayake. Forecasting of seasonal and monthly rainfall in Sri Lanka using Climate Predictability Tool (CPT). Dhaka : SAARC Meteorological Research Centre, 2009.

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ICOM International Committee for Museology. Symposium. Forecasting, a museological tool ? : Museology and futurology = La prospective, un outil museologique ? : museologie et futurologie. [Stockholm, Sweden] : International Committee for Museology, 1995.

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Farley, Alan S. The master swing trader tool kit : The market survival guide. New York, USA : McGraw-Hill, 2010.

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The master swing trader tool kit : The market survival guide. New York : McGraw-Hill, 2010.

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Chapitres de livres sur le sujet "FORECASTING TOOL"

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Heimgaertner, Florian, Thomas Sachs et Michael Menth. « ClassCast : A Tool for Class-Based Forecasting ». Dans Lecture Notes in Computer Science, 322–26. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74947-1_26.

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Michez, Bernard. « Ageing Factors and Forecasting Tool for Companies ». Dans Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021), 3–9. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74605-6_1.

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Porter, James, Gerald Day, John C. Schaake et Lucien Wang. « New York City’s Operations Support Tool : Utilizing Hydrologic Forecasts for Water Supply Management ». Dans Handbook of Hydrometeorological Ensemble Forecasting, 1–42. Berlin, Heidelberg : Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-40457-3_56-1.

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Porter, James, Gerald Day, John C. Schaake et Lucien Wang. « New York City’s Operations Support Tool : Utilizing Hydrologic Forecasts for Water Supply Management ». Dans Handbook of Hydrometeorological Ensemble Forecasting, 1329–70. Berlin, Heidelberg : Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-39925-1_56.

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Curci, Gabriele. « An Air Quality Forecasting Tool over Italy (ForeChem) ». Dans Air Pollution Modeling and its Application XXI, 397–401. Dordrecht : Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1359-8_67.

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Hoffman, Eric G. « Surface Potential Temperature as an Analysis and Forecasting Tool ». Dans Synoptic—Dynamic Meteorology and Weather Analysis and Forecasting, 163–81. Boston, MA : American Meteorological Society, 2008. http://dx.doi.org/10.1007/978-0-933876-68-2_8.

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Adamuthe, Amol C., et Ramkrishna V. Vhatkar. « Improved Neural Network Tool : Application to Societal Forecasting Problems ». Dans Techno-Societal 2018, 3–10. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16962-6_1.

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Lyneis, James M., et Maurice A. Glucksman. « Market Analysis and Forecasting as a Strategic Business Tool ». Dans Computer-Based Management of Complex Systems, 136–43. Berlin, Heidelberg : Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74946-9_13.

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Beser, Muriel, et Staffan Algers. « SAMPERS — The New Swedish National Travel Demand Forecasting Tool ». Dans National Transport Models, 101–18. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04853-5_9.

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Ibargüengoytia, Pablo H., Alberto Reyes, Inés Romero, David Pech, Uriel A. García et Mónica Borunda. « A Tool for Learning Dynamic Bayesian Networks for Forecasting ». Dans Advances in Artificial Intelligence and Its Applications, 520–30. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27101-9_40.

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Actes de conférences sur le sujet "FORECASTING TOOL"

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Bibik, Vladislav. « Tool life forecasting ». Dans 2012 7th International Forum on Strategic Technology (IFOST). IEEE, 2012. http://dx.doi.org/10.1109/ifost.2012.6357710.

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Mercier, Olivier, Stephane Dupin, Cédric Ulmer et Johannes Demund. « Forecasting tool on a mobile device ». Dans 23rd French Speaking Conference. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/2044354.2044363.

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Chis, Violeta, Constantin Barbulescu, Stefan Kilyeni et Simona Dzitac. « Short-term load forecasting software tool ». Dans 2018 7th International Conference on Computers Communications and Control (ICCCC). IEEE, 2018. http://dx.doi.org/10.1109/icccc.2018.8390446.

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Pakhomov, Andrew L., Valery F. Kalinin, Boris A. Makeev et Alexandra V. Zueva. « APL as a tool for scientific forecasting ». Dans the international conference. New York, New York, USA : ACM Press, 1992. http://dx.doi.org/10.1145/144045.144117.

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Lin, Kuo-Yi, et Jeffrey J. P. Tsai. « A Deep Learning-Based Customer Forecasting Tool ». Dans 2016 IEEE Second International Conference on Multimedia Big Data (BigMM). IEEE, 2016. http://dx.doi.org/10.1109/bigmm.2016.85.

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Chang, Grace, Ann Dallman, Kaustubha Raghukumar, Mohammad Khalil, Jeremy Kasper, Craig Jones et Jesse Roberts. « Wave Energy Production Optimization and Forecasting Tool. » Dans Proposed for presentation at the International Conference on Ocean Energy (ICOE) 2021. US DOE, 2021. http://dx.doi.org/10.2172/1862634.

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Schiopu, Raluca, Constantin Barbulescu, Stefan Kilyeni, Antheia Deacu et Alin Vernica. « ANN backpropagation power consumption forecasting ». Dans IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON). IEEE, 2015. http://dx.doi.org/10.1109/eurocon.2015.7313774.

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Werntz, David, Steven Loyola et Silvino Zendejas. « FASTER - A tool for DSN forecasting and scheduling ». Dans 9th Computing in Aerospace Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-4500.

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Filipe, J. M., R. J. Bessa, J. Sumaili, R. Tome et J. N. Sousa. « A hybrid short-term solar power forecasting tool ». Dans 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP). IEEE, 2015. http://dx.doi.org/10.1109/isap.2015.7325543.

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Sharifov, Anar Rabilovich, Dmitry Sergeevich Perets, Ivan Aleksandrovich Zhdanov, Evgenii Valerievich Belonogov et Andrei Stanislavovich Margarit. « Tool for Operational Well Stock Management and Forecasting ». Dans SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/201927-ms.

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Rapports d'organisations sur le sujet "FORECASTING TOOL"

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Manata, Jack P. Tool Life Analysis and Forecasting : 2. Forecasting Tool Life Using Neural Networks. Fort Belvoir, VA : Defense Technical Information Center, mars 1993. http://dx.doi.org/10.21236/ada266919.

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Manata, Jack P. Tool Life Analysis and Forecasting : 1. Statistical Analysis. Fort Belvoir, VA : Defense Technical Information Center, mars 1993. http://dx.doi.org/10.21236/ada266918.

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Omar, Farhad, et David Holmberg. Load Forecasting Tool for NIST Transactive Energy Market. National Institute of Standards and Technology, septembre 2021. http://dx.doi.org/10.6028/nist.tn.2181.

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Ripley, Royal S. Converting the JNEM Training Aid to a Forecasting Tool. Fort Belvoir, VA : Defense Technical Information Center, décembre 2008. http://dx.doi.org/10.21236/ada499702.

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Smith, Jane M., Mary E. Anderson, Alexandros A. Taflanidis, Andrew B. Kennedy, Joannes J. Westerink et Kwok F. Cheung. HAKOU v3 : SWIMS Hurricane Inundation Fast Forecasting Tool for Hawaii. Fort Belvoir, VA : Defense Technical Information Center, février 2012. http://dx.doi.org/10.21236/ada559303.

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Baluga, Anthony, et Masato Nakane. Maldives Macroeconomic Forecasting :. Asian Development Bank, décembre 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Peterson, Warren. PR-663-20208-Z01 CO2e Economic Analysis Tool. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), mars 2021. http://dx.doi.org/10.55274/r0012079.

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The CO2e Economic Analysis Tool (CEAT) is a spreadsheet-based application for comparing project alternatives that are sensitive to GHG emission rates, emission levies or other financial parameters. The tool is applicable to hydrocarbon transportation systems, with an emphasis on natural gas transmission. CEAT provides a comparative forecast of benefits and expenses (including levies) from initial cash flow to arrival at the forecast horizon. Along with financial forecasting functions, the tool estimates the emissions associated with a wide range of hydrocarbon fluids (gas and liquid), electricity, thermal energy, and upstream transportation. The forecast model provides flexible configuration of CAPEX and O and M expenses and a customizable levy structure. The tool is Excel-based and requires version 16 or newer.
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Peterson, Warren. PR-663-20208-Z03 CO2e Economic Analysis Tool. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), avril 2023. http://dx.doi.org/10.55274/r0012255.

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The CO2e Economic Analysis Tool (CEAT) is a spreadsheet-based application for comparing project alternatives that are sensitive to GHG emission rates, emission levies, or other financial parameters. The tool is applicable to hydrocarbon transportation systems, with an emphasis on natural gas transmission. CEAT provides a comparative forecast of benefits and expenses (including levies) from initial cash flow to arrival at the forecast horizon. Along with financial forecasting functions, the tool estimates the emissions associated with a wide range of hydrocarbon fluids (gas and liquid), electricity, thermal energy, and upstream transportation. The forecast model provides flexible configuration of CAPEX and O and M expenses and a customizable levy structure. This download includes a copy of the report and a copy of the Excel-based CO2e Economic Analysis Tool software (aka CEAT). The spreadsheet and the associated report are licensed to single users as noted in the end-user license agreement that is contained in the zip file.
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Fritts, David C. Creation of a Dynamical Stratospheric Turbulence Forecasting and Nowcasting Tool for High Altitude Airships and Other Aircraft. Fort Belvoir, VA : Defense Technical Information Center, octobre 2008. http://dx.doi.org/10.21236/ada487617.

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Letcher, Theodore, Sandra LeGrand et Christopher Polashenski. The Blowing Snow Hazard Assessment and Risk Prediction model : a Python based downscaling and risk prediction for snow surface erodibility and probability of blowing snow. Engineer Research and Development Center (U.S.), mars 2022. http://dx.doi.org/10.21079/11681/43582.

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Blowing snow is an extreme terrain hazard causing intermittent severe reductions in ground visibility and snow drifting. These hazards pose significant risk to operations in snow-covered regions. While many ingredients-based forecasting methods can be employed to predict where blowing snow is likely to occur, there are currently no physically based tools to predict blowing snow from a weather forecast. However, there are several different process models that simulate the transport of snow over short distances that can be adapted into a terrain forecasting tool. This report documents a downscaling and blowing-snow prediction tool that leverages existing frameworks for snow erodibility, lateral snow transport, and visibility, and applies these frameworks for terrain prediction. This tool is designed to work with standard numerical weather model output and user-specified geographic models to generate spatially variable forecasts of snow erodibility, blowing snow probability, and deterministic blowing-snow visibility near the ground. Critically, this tool aims to account for the history of the snow surface as it relates to erodibility, which further refines the blowing-snow risk output. Qualitative evaluations of this tool suggest that it can provide more precise forecasts of blowing snow. Critically, this tool can aid in mission planning by downscaling high-resolution gridded weather forecast data using even higher resolution terrain dataset, to make physically based predictions of blowing snow.
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