Academic literature on the topic 'FORECASTING TOOL'
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Journal articles on the topic "FORECASTING TOOL"
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.
Full textLindh, 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.
Full textMaksarov, 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.
Full textKossov, 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.
Full text&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.
Full textSimpson, Sarah. "From Research Model to Forecasting Tool." Space Weather 1, no. 1 (October 2003): n/a. http://dx.doi.org/10.1029/2003sw000029.
Full textDiez-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.
Full textJia, 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.
Full textLawnik, 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.
Full textZverev, 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.
Full textDissertations / Theses on the topic "FORECASTING TOOL"
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.
Full textThe 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.
Seemann, Thomas. "Prediction markets as forecasting tool in decision processes." Berlin Pro Business, 2008. http://d-nb.info/993592570/04.
Full textLi, 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.
Full textYaufman, 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.
Full textChoi, 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.
Full textBRITO, 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.
Full textA 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.
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.
Full textIncludes bibliographical references (leaves 74-75).
by Alison L. Page.
S.M.
M.B.A.
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.
Full textAccelerating 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.
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.
Full textThesis 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.
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.
Full textDepartment 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.
Books on the topic "FORECASTING TOOL"
Inc, Savant, and National Association of State Units on Aging., eds. FOCUS, forecasting tool screen documentation. [Washington, D.C.?]: Savant, Incorporated, Natl. Association of State Units on Aging, 1987.
Find full textOutwater, Maren. Developing a Method Selection Tool for Travel Forecasting. Washington, D.C.: Transportation Research Board, 2017. http://dx.doi.org/10.17226/24931.
Full textGodet, Michel. Futures studies: A tool-box for problem solving. Paris: GERPA/Futuribles, 1991.
Find full textB. R. S. B. Basnayake. Seasonal weather forecasting in Bangladesh using Climate Predictability Tool (CPT). Dhaka: SAARC Meteorological Research Centre, 2010.
Find full textBengoechea, Pilar. A useful tool to identify recessions in the Euro-area. Brussels: European Commission, Directorate-General for Economic and Financial Affairs, 2004.
Find full textBasnayake, B. R. S. B. Forecasting of seasonal and monthly rainfall in Sri Lanka using Climate Predictability Tool (CPT). Dhaka: SAARC Meteorological Research Centre, 2009.
Find full textB. R. S. B. Basnayake. Forecasting of seasonal and monthly rainfall in Sri Lanka using Climate Predictability Tool (CPT). Dhaka: SAARC Meteorological Research Centre, 2009.
Find full textICOM 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.
Find full textFarley, Alan S. The master swing trader tool kit: The market survival guide. New York, USA: McGraw-Hill, 2010.
Find full textThe master swing trader tool kit: The market survival guide. New York: McGraw-Hill, 2010.
Find full textBook chapters on the topic "FORECASTING TOOL"
Heimgaertner, Florian, Thomas Sachs, and Michael Menth. "ClassCast: A Tool for Class-Based Forecasting." In Lecture Notes in Computer Science, 322–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74947-1_26.
Full textMichez, Bernard. "Ageing Factors and Forecasting Tool for Companies." In 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.
Full textPorter, James, Gerald Day, John C. Schaake, and Lucien Wang. "New York City’s Operations Support Tool: Utilizing Hydrologic Forecasts for Water Supply Management." In 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.
Full textPorter, James, Gerald Day, John C. Schaake, and Lucien Wang. "New York City’s Operations Support Tool: Utilizing Hydrologic Forecasts for Water Supply Management." In 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.
Full textCurci, Gabriele. "An Air Quality Forecasting Tool over Italy (ForeChem)." In 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.
Full textHoffman, Eric G. "Surface Potential Temperature as an Analysis and Forecasting Tool." In 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.
Full textAdamuthe, Amol C., and Ramkrishna V. Vhatkar. "Improved Neural Network Tool: Application to Societal Forecasting Problems." In Techno-Societal 2018, 3–10. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16962-6_1.
Full textLyneis, James M., and Maurice A. Glucksman. "Market Analysis and Forecasting as a Strategic Business Tool." In 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.
Full textBeser, Muriel, and Staffan Algers. "SAMPERS — The New Swedish National Travel Demand Forecasting Tool." In National Transport Models, 101–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04853-5_9.
Full textIbargüengoytia, Pablo H., Alberto Reyes, Inés Romero, David Pech, Uriel A. García, and Mónica Borunda. "A Tool for Learning Dynamic Bayesian Networks for Forecasting." In 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.
Full textConference papers on the topic "FORECASTING TOOL"
Bibik, Vladislav. "Tool life forecasting." In 2012 7th International Forum on Strategic Technology (IFOST). IEEE, 2012. http://dx.doi.org/10.1109/ifost.2012.6357710.
Full textMercier, Olivier, Stephane Dupin, Cédric Ulmer, and Johannes Demund. "Forecasting tool on a mobile device." In 23rd French Speaking Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2044354.2044363.
Full textChis, Violeta, Constantin Barbulescu, Stefan Kilyeni, and Simona Dzitac. "Short-term load forecasting software tool." In 2018 7th International Conference on Computers Communications and Control (ICCCC). IEEE, 2018. http://dx.doi.org/10.1109/icccc.2018.8390446.
Full textPakhomov, Andrew L., Valery F. Kalinin, Boris A. Makeev, and Alexandra V. Zueva. "APL as a tool for scientific forecasting." In the international conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/144045.144117.
Full textLin, Kuo-Yi, and Jeffrey J. P. Tsai. "A Deep Learning-Based Customer Forecasting Tool." In 2016 IEEE Second International Conference on Multimedia Big Data (BigMM). IEEE, 2016. http://dx.doi.org/10.1109/bigmm.2016.85.
Full textChang, Grace, Ann Dallman, Kaustubha Raghukumar, Mohammad Khalil, Jeremy Kasper, Craig Jones, and Jesse Roberts. "Wave Energy Production Optimization and Forecasting Tool." In Proposed for presentation at the International Conference on Ocean Energy (ICOE) 2021. US DOE, 2021. http://dx.doi.org/10.2172/1862634.
Full textSchiopu, Raluca, Constantin Barbulescu, Stefan Kilyeni, Antheia Deacu, and Alin Vernica. "ANN backpropagation power consumption forecasting." In IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON). IEEE, 2015. http://dx.doi.org/10.1109/eurocon.2015.7313774.
Full textWerntz, David, Steven Loyola, and Silvino Zendejas. "FASTER - A tool for DSN forecasting and scheduling." In 9th Computing in Aerospace Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-4500.
Full textFilipe, J. M., R. J. Bessa, J. Sumaili, R. Tome, and J. N. Sousa. "A hybrid short-term solar power forecasting tool." In 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP). IEEE, 2015. http://dx.doi.org/10.1109/isap.2015.7325543.
Full textSharifov, Anar Rabilovich, Dmitry Sergeevich Perets, Ivan Aleksandrovich Zhdanov, Evgenii Valerievich Belonogov, and Andrei Stanislavovich Margarit. "Tool for Operational Well Stock Management and Forecasting." In SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/201927-ms.
Full textReports on the topic "FORECASTING TOOL"
Manata, Jack P. Tool Life Analysis and Forecasting: 2. Forecasting Tool Life Using Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada266919.
Full textManata, Jack P. Tool Life Analysis and Forecasting: 1. Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada266918.
Full textOmar, Farhad, and David Holmberg. Load Forecasting Tool for NIST Transactive Energy Market. National Institute of Standards and Technology, September 2021. http://dx.doi.org/10.6028/nist.tn.2181.
Full textRipley, Royal S. Converting the JNEM Training Aid to a Forecasting Tool. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada499702.
Full textSmith, Jane M., Mary E. Anderson, Alexandros A. Taflanidis, Andrew B. Kennedy, Joannes J. Westerink, and Kwok F. Cheung. HAKOU v3: SWIMS Hurricane Inundation Fast Forecasting Tool for Hawaii. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada559303.
Full textBaluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.
Full textPeterson, Warren. PR-663-20208-Z01 CO2e Economic Analysis Tool. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2021. http://dx.doi.org/10.55274/r0012079.
Full textPeterson, Warren. PR-663-20208-Z03 CO2e Economic Analysis Tool. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2023. http://dx.doi.org/10.55274/r0012255.
Full textFritts, 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, October 2008. http://dx.doi.org/10.21236/ada487617.
Full textLetcher, Theodore, Sandra LeGrand, and 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.), March 2022. http://dx.doi.org/10.21079/11681/43582.
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