Bibliografie tematiche / Forecasting – Research

Letteratura scientifica selezionata sul tema "Forecasting – Research"

Autore: Grafiati
Pubblicato: 25 luglio 2024

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Articoli di riviste sul tema "Forecasting – Research"

1

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

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Wind power has entered a rapid progress stage. Due to the intermittency of wind energy and the non-linearity of power system, there exist many uncertain variables which should be considered in the wind power prediction. Accurate wind power forecastings are beneficial for wind plant operators, utility operators, and utility customers. The current forecasting methods include persistence method, physical method, statistical method, and the comprehensive one combing all the other methods. This paper provides a detail review on wind speed and wind power forecasting methods based on recent available published papers. Several forecasting models were discussed and a lot of researchers on the models, which have their own characteristics, were presented. An overview of comparative analysis of wind forecasting time scales is discussed as well.
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Diamantopoulos, A. "Research on Forecasting". International Journal of Forecasting 18, n. 3 (luglio 2002): 479–80. http://dx.doi.org/10.1016/s0169-2070(02)00003-1.

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Adya, Monica. "Research on Forecasting". International Journal of Forecasting 18, n. 3 (luglio 2002): 481–82. http://dx.doi.org/10.1016/s0169-2070(02)00005-5.

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Chatfield, Chris. "Research on Forecasting". International Journal of Forecasting 15, n. 2 (aprile 1999): 225–26. http://dx.doi.org/10.1016/s0169-2070(98)00078-8.

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Sanders, Nada R. "Research on Forecasting". International Journal of Forecasting 15, n. 3 (luglio 1999): 345–46. http://dx.doi.org/10.1016/s0169-2070(99)00010-2.

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Fildes, Robert. "Research on forecasting". International Journal of Forecasting 5, n. 1 (gennaio 1989): 151–53. http://dx.doi.org/10.1016/0169-2070(89)90081-2.

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Vasilevskaya, L. N., e E. Yu Potalova. "Hydrometeorological research and forecasting". Hydrometeorological research and forecasting, n. 1 (20 marzo 2020): 6–20. http://dx.doi.org/10.37162/2618-9631-2020-1-6-20.

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Scott Armstrong, J. "Research needs in forecasting". International Journal of Forecasting 4, n. 3 (gennaio 1988): 449–65. http://dx.doi.org/10.1016/0169-2070(88)90111-2.

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Evanschitzky, Heiner, e J. Scott Armstrong. "Replications of forecasting research". International Journal of Forecasting 26, n. 1 (gennaio 2010): 4–8. http://dx.doi.org/10.1016/j.ijforecast.2009.09.003.

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Boylan, John E., Paul Goodwin, Maryam Mohammadipour e Aris A. Syntetos. "Reproducibility in forecasting research". International Journal of Forecasting 31, n. 1 (gennaio 2015): 79–90. http://dx.doi.org/10.1016/j.ijforecast.2014.05.008.

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Tesi sul tema "Forecasting – Research"

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Bruno, Jack H. "Evaluating the Weather Research and Forecasting Model Fidelity for Forecasting Lake Breezes". Ohio University Honors Tutorial College / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1556189524538244.

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Nissan, Hannah. "Modelling rainfall erosivity using the Weather Research and Forecasting model". Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24681.

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Soil erosion is a serious threat to agricultural productivity and the sustainable provision of food to a growing world population. Current erosion models employ simplistic treatments of rainfall. This thesis presents a new approach to erosion modelling, using the Weather Research and Forecasting model to simulate rainfall erosivity, an indicator of the erosive capacity of rain. Rainfall erosivity is modelled in the Caucasus region, an area vulnerable to erosion and climate change pressures. Low intensity rainfall (below 2 mmhr^{-1}) is found to contribute significantly to erosivity (23%), contrary to common assumptions. An exponential dependence of the fraction of erosivity from light rain on the proportion of light rain is found. Erosion models focus on storms, but results suggest that storm-based calculations may exclude up to 30% of erosivity. In the Universal Soil Loss Equation, this does not lead to errors in long term soil loss but could cause an underestimation of event erosion. Rainfall kinetic energy flux is an important variable in erosion prediction and is routinely parameterised from intensity. Here this is dynamically simulated from basic physics in a cloud resolving model, using four microphysics schemes. Results are within the range of observations and capture the observed variability in kinetic energy for a given intensity, where current methods fail. Large raindrops are shown to contribute disproportionately to total kinetic energy, and also to surface precipitation, compared with their number. No connection has hitherto been drawn between aerosols and soil erosion. The effect of aerosols on rainfall erosivity is investigated in a cloud resolving model. Aerosols can either enhance or suppress precipitation. In both these cases the response of erosivity to a rise in aerosols is in the same direction as, but amplified beyond, the change in total rain. It is also shown that aerosols can influence erosivity by changing raindrop sizes. These results suggest that anthropogenic aerosol emissions affect erosivity and thus may have important consequences for agricultural productivity.
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McCarty, Laura Smith. "Evaluation and recommendation of storage space forecasting model(s)". Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/24250.

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Mehalic, Charles J. "Multiparameter forecasting techniques for the Marine Corps officer rate generator". Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA241453.

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Abstract (sommario):
Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 1990.
Thesis Advisor(s): Read, Robert R. Second Reader: Whitaker, Lyn R. "September 1990." Description based on title screen as viewed on March 19, 2010. DTIC Identifier(s): Forecast, Seasonality, Attrition Estimation, Harrison, Winters, Bayesian, Expotential Smoothing, Shrinkage, Aggregation. Author(s) subject terms: Forecast, Attrition Estimation, Harrison, Winters, Bayesian, Seasonality, Expotential smoothing, Shrinkage, Aggregation. Includes bibliographical references (p. 100-101). Also available in print.
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Hong, Tao. "Long-Term Spatial Load Forecasting Using Human-Machine Co-construct Intelligence Framework". NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-10212008-105450/.

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This thesis presents a formal study of the long-term spatial load forecasting problem: given small area based electric load history of the service territory, current and future land use information, return forecast load of the next 20 years. A hierarchical S-curve trending method is developed to conduct the basic forecast. Due to uncertainties of the electric load data, the results from the computerized program may conflict with the nature of the load growth. Sometimes, the computerized program is not aware of the local development because the land use data lacks such information. A human-machine co-construct intelligence framework is proposed to improve the robustness and reasonability of the purely computerized load forecasting program. The proposed algorithm has been implemented and applied to several utility companies to forecast the long-term electric load growth in the service territory and to get satisfying results.
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Inman, Oliver Lane. "Technology Forecasting Using Data Envelopment Analysis". PDXScholar, 2004. https://pdxscholar.library.pdx.edu/open_access_etds/2682.

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The ability to anticipate future capabilities of technology products has broad implications for organizations. Technological forecasting allows management to improve resource allocation, make better staffing decisions, and more confidently plan facilities and capital expenditures. Technology forecasting may also identify potential new markets and opportunities, such as finding ways to exploit current technology beyond its originally intended purposes. Modern technology forecasters use an array of forecasting methods to predict the future performance of a technology, such as time-series analysis, regression, stochastic methods, and simulation. These quantitative methods rely on the assumption that past behavior will continue. Shortcomings include their lack of emphasis on the best technology available and the fact that they do not effectively address the dynamic nature of ever changing trade-off surfaces. This research proposes a new method to address the shortcomings of common forecasting techniques by extending a well-established management science methodology known as data envelopment analysis (DEA). This new method is referred to as Technology Forecasting with Data Envelopment Analysis (TFDEA). Three case studies are examined to determine the method's validity. The first case study is that of relational database system performance based upon industry benchmarks obtained from the Transaction Processing Performance Council (TPC). The results reveal that TFDEA provides a more accurate picture of the state of the art than basic regression. The second case study expands Moore's law to six dimensions, resulting in a more comprehensive assessment of microprocessor technology. The final case study re-examines hard disk drive data for the years 1994-1999 in order to evaluate the technological progress of multiple technological approaches presented in Christensen's The Innovator's Dilemma . Major contributions include both a new technology forecasting technique and an important extension of the temporal DEA methodology, which together offer a new and more comprehensive method for evaluating and forecasting technology.
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Freixieiro, Gomes de Mello Rafael. "Design-led future forecasting model for mobile communications". Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13142.

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Since the establishment of the wireless sector, design has been playing a wide range of roles. Specialized literature has been extensively reporting the use of this discipline associated with NPD, focusing on hardware and software development in the mobile communications. On the other hand, evidences of its use to support forecasting are scarce and generic. Finally, formal publications addressing future forecasting from a design perspective in the context of mobile communications have never been reported, leading to a knowledge gap that needs to be addressed. This research investigates the strategic roles, applications and contributions of design and designers for future forecasting in the wireless telecom sector. As a comprehensive discipline, it is used to examine and identify a number of factors that might influence/impact in the development of visionary solutions supporting the design team of traditional handset manufacturers to make better decisions in order to ‘shape’ the future in the wireless industry. Considering these ideas, the aim of this research is to create a ‘design-led future forecasting model for mobile communications’ to assist and support traditional manufacturers’ design team. This PhD study relies on a qualitative methodology comprising a number of data collection and analysis tools (e.g. literature review, case studies analysis, in-depth experts’ interviews, workshops and Grounded Theory). To create the intended framework, extensive secondary and primary data; theoretical and practical inputs were brought together, analysed and combined. The proposed model was evaluated through two rounds of experts’ interviews complemented by two workshops with potential users (e.g. design students) to check and explore its practicalities when applied to design for the future. Finally, this study bridges future forecasting and the wireless telecom through the use of design to address the literature gap. The richness of the developed model provides practical assistance to traditional manufacturer’s design team informing about a broad spectrum of aspects that should be considered when designing for the future in the mobile telecom industry, supporting strategic decision making in different stages of the future-led design process.
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Calmon, André du Pin. "Reverse logistics for consumer electronics : forecasting failures, managing inventory, and matching warranties". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98720.

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Abstract (sommario):
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 147-150).
The goal of this thesis is to describe, model, and optimize reverse logistics systems commonly used in the Consumer Electronics industry. The context and motivation for this work stem from a collaboration with an industrial partner, a Fortune 500 company that sells consumer electronics and is one of the top retailers in its sector. The thesis is divided into three parts. In the first part of the thesis we model and analyze the problem of forecasting failures of new products. When a new device is introduced to the market there is limited information available about its failure time distribution since most devices have yet to fail. However, there is extensive failure time data for prior devices, as well as evidence that the failure time distribution for new devices can be forecast from the data for prior devices. In this setting, we propose two strategies for forecasting the failure distribution of new products that leverages the censored failure observations for the new devices as well as this massive amount of data collected for prior devices. We validate these strategies using data from our industrial partner and using data from a social enterprise located in the Boston area. The second part of the thesis concerns inventory management in a reverse logistics system that supports the warranty returns and replacement for a consumer electronic device. This system is a closed-loop supply chain since failed devices are refurbished and are kept in inventory to be used as replacement devices or are sold through a side-sales channel. Furthermore, managing inventory in this system is challenging due to the short life-cycle of this type of device and the rapidly declining value for the inventory that could potentially be sold. We propose a stochastic model that captures the dynamics of inventory of this system, including the limited life-cycle and the declining value of inventory that can be sold off. We characterize the structure of the optimal policy for this problem. In addition, we introduce two heuristics: (i) a certainty-equivalent approximation, which leads to a simple closed form policy; and (ii) a dual balancing heuristic, which results in a more tractable newsvendor type model. We also develop a robust version of this model in order to obtain bounds for the overall performance of the system. The performance of these heuristics is analyzed using data from our industrial partner. The final part of the thesis concerns the problem faced by a consumer electronics retailer when matching devices in inventory to customers. More specifically, we analyze a setting where there are two warranties in place: (i) the consumer warranty, offered by the retailer to the consumer, and (ii) the Original Equipment Manufacturer (OEM) warranty, offered by the OEM to the retailer. Both warranties are valid for a limited period (usually 12 months), and once warranties expire, the coverage to replace or repair a faulty device ends. Thus, a customer does not receive a replacement if he/she is out of consumer warranty, and the retailer cannot send the device to the OEM for repairs if it is out of OEM warranty. The retailer would ideally like to have the two warranties for a device being matched, i.e., the customer would have the same time left in his consumer warranty as the device would have left in the OEM warranty. A mismatch between these warranties can incur costs to the retailer beyond the usual processing costs of warranty requests. Namely, since a device can fail multiple times during its lifecycle the replacement device sent to customers that file warranty requests can lead to out-of-OEM-warranty returns. In order to mitigate the number of out-of-OEM-warranty returns, we propose an online algorithm to match customers that have filed warranty claims to refurbished devices in inventory. The algorithm matches the oldest devices in inventory to the oldest customers in each period. We characterize the competitive ratio of this algorithm and, through numerical experiments using historical data, demonstrate that it can significantly reduce out of warranty returns compared to our partner's current strategy.
by Andre du Pin Calmon.
Ph. D.
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Chan, San-wing Frederick. "Developing inquiry based learning in secondary geography education topic weather forecast : an action research /". Click to view the E-thesis via HKUTO, 2003. http://sunzi.lib.hku.hk/hkuto/record/B3984870X.

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Conatser, Dean G. "Forecasting U.S. Marine Corps reenlistments by military occupational specialty and grade". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FConatser.pdf.

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Abstract (sommario):
Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2006.
Thesis Advisor(s): Ronald D. Fricker. "September 2006." Includes bibliographical references (p. 49-50). Also available in print.
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Libri sul tema "Forecasting – Research"

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Chow, Fotini K., Stephan F. J. De Wekker e Bradley J. Snyder, a cura di. Mountain Weather Research and Forecasting. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-4098-3.

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Erhardt, Gregory D., Jawad Hoque, Mei Chen, Reginald Souleyrette, David Schmitt, Ankita Chaudhary, Sujith Rapolu et al. Traffic Forecasting Accuracy Assessment Research. Washington, D.C.: Transportation Research Board, 2020. http://dx.doi.org/10.17226/25637.

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Clifton, Peter. Market research: Using forecasting in business. Oxford: Butterworth-Heinemann, 1992.

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Clifton, Peter. Market research: Using forecasting in business. London: Butterworth Heineman, 1992.

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Neves, Diogo F. Droughts: New research. Hauppauge, N.Y: Nova Science Publisher's, 2011.

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Mentzer, John T. Sales forecasting management. Thousand Oaks, CA: Sage Publications, 1998.

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Giaiotti, Dario B., Reinhold Steinacker e Fulvio Stel, a cura di. Atmospheric Convection: Research and Operational Forecasting Aspects. Vienna: Springer Vienna, 2007. http://dx.doi.org/10.1007/978-3-211-69291-2.

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B, Giaiotti Dario, Steinacker R e Stel Fulvio, a cura di. Atmospheric convection: Research and operational forecasting aspects. Wien: Springer, CISM, 2007.

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C, Bienstock Carol, a cura di. Sales forecasting management: Understanding the techniques, systems, and management of the sales forecasting process. Thousand Oaks: Sage Publications, 1998.

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Bolt, Gordon J. Market and sales forecasting. New York: Watts, 1988.

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Capitoli di libri sul tema "Forecasting – Research"

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Isaac, David. "Forecasting and Research". In Property Investment, 292–302. London: Macmillan Education UK, 1998. http://dx.doi.org/10.1007/978-1-349-14468-6_14.

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Graefe, Andreas, Kesten C. Green e J. Scott Armstrong. "Forecasting". In Encyclopedia of Operations Research and Management Science, 593–604. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_357.

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Zheng, Yafei, Kin Keung Lai e Shouyang Wang. "Existing research". In Forecasting Air Travel Demand, 10–33. Abingdon, Oxon ; New York, NY : Routledge, 2018. | Series: Routledge advances in risk management ; 11: Routledge, 2018. http://dx.doi.org/10.4324/9781351215503-2.

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Burley, T. A., e G. O’Sullivan. "Forecasting Methods". In Work Out Operational Research, 189–212. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-08157-8_13.

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Zheng, Xiaolian, e Ben M. Chen. "Future Research". In Stock Market Modeling and Forecasting, 149–50. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_8.

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Zheng, Yafei, Kin Keung Lai e Shouyang Wang. "Conclusions and future research". In Forecasting Air Travel Demand, 143–45. Abingdon, Oxon ; New York, NY : Routledge, 2018. | Series: Routledge advances in risk management ; 11: Routledge, 2018. http://dx.doi.org/10.4324/9781351215503-9.

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Allen, P. Geoffrey, e Robert Fildes. "Econometric Forecasting". In International Series in Operations Research & Management Science, 303–62. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-0-306-47630-3_15.

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Ahlburg, Dennis A. "Population Forecasting". In International Series in Operations Research & Management Science, 557–75. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-0-306-47630-3_25.

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Nikolopoulos, Konstantinos I., e Dimitrios D. Thomakos. "Forecasting Analytics". In International Series in Operations Research & Management Science, 381–420. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-68837-4_12.

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Lütkepohl, Helmut. "Forecasting Unpredictable Variables". In Empirical Economic and Financial Research, 287–304. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03122-4_18.

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Atti di convegni sul tema "Forecasting – Research"

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Pace, Clark B. "Labor Availability and Productivity Forecasting". In Construction Research Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40671(2003)111.

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Zhendong Chen e Chaozhen Guo. "Research of forecasting typhoon GDSS". In 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icicisys.2010.5658851.

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Bi Ruixiang. "Fiscal revenue forecasting system research". In 2010 International Conference on Future Information Technology and Management Engineering (FITME). IEEE, 2010. http://dx.doi.org/10.1109/fitme.2010.5654754.

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Wijnhoven, Fons, Nathan De Bruijn e Robin Effing. "Google trends forecasting of youth employment". In CARMA 2024 - 6th International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2024. http://dx.doi.org/10.4995/carma2024.2024.17158.

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The forecasting field has been using the surge in big data and advanced computational capabilities. This article discusses the methodological issues of Google Trends (GT) data reliability and forecasting validity for youth unemployment forecasts. We demonstrate the problems with static GT forecasting procedures and show a 44% increase in forecasting accuracy by applying time-varying model respecification forecasting.
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Wong, James M. W., S. Thomas Ng, Albert P. C. Chan e Y. H. Chiang. "Forecasting Construction Manpower Demand: An Econometric Model". In Construction Research Congress 2009. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41020(339)97.

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Liu, Shuliang, Zhiqiang Hu e Xiukai Chi. "The Research of Power Load Forecasting Method on Combination Forecasting Model". In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.1273.

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Feroz, Raja M. Asim, e Adeel Javed. "Forecasting of Wind Resources Using the Weather Research and Forecasting Software". In 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST). IEEE, 2021. http://dx.doi.org/10.1109/ibcast51254.2021.9393212.

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Fang, Kui, Qingshan Ren, Xiangmei Feng e Xinghui Zhu. "Research on Rice Yield Forecasting Model". In 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/amms-17.2017.26.

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Qin, Wan, Wei Yong e Yang Xiongqiong. "Research on grey wave forecasting model". In 2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/gsis.2009.5408288.

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Zhan, Zhigang. "Research on Power Demand Forecasting Methods". In 2nd International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/mseee-18.2018.44.

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Rapporti di organizzazioni sul tema "Forecasting – Research"

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Glascoe, L. Wind Energy Forecasting with the Weather Research and Forecasting Model, CRADA No. TC02123.0. Office of Scientific and Technical Information (OSTI), marzo 2011. http://dx.doi.org/10.2172/1038916.

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Glascoe, L., e L. Kirkpatrick. Wind Energy Forecasting with the Weather Research and Forecasting Model, CRADA No. TC02123.0. Office of Scientific and Technical Information (OSTI), marzo 2021. http://dx.doi.org/10.2172/1773576.

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Sauter, Barbara. Weather Research and Forecasting (WRF) Results Over New Mexico. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2006. http://dx.doi.org/10.21236/ada443014.

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Dugan, John. Assessment of European Research Programs Supporting Coastal Ocean Forecasting Systems. Fort Belvoir, VA: Defense Technical Information Center, aprile 1997. http://dx.doi.org/10.21236/ada325813.

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Poppeliers, Christian, Katherine Aur e Leiph Preston. Predicting Atmospheric Green's Functions using the Weather Research and Forecasting Model. Office of Scientific and Technical Information (OSTI), dicembre 2018. http://dx.doi.org/10.2172/1761090.

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Reen, Brian P. Improving Weather Research and Forecasting Model Initial Conditions via Surface Pressure Analysis. Fort Belvoir, VA: Defense Technical Information Center, settembre 2015. http://dx.doi.org/10.21236/ada621305.

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7

Iacono, Michael J. Enhancing Cloud Radiative Processes and Radiation Efficiency in the Advanced Research Weather Research and Forecasting (WRF) Model. Office of Scientific and Technical Information (OSTI), marzo 2015. http://dx.doi.org/10.2172/1172166.

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Lundquist, Katherine Ann. Implementation of the Immersed Boundary Method in the Weather Research and Forecasting model. Office of Scientific and Technical Information (OSTI), gennaio 2006. http://dx.doi.org/10.2172/900883.

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9

Alice Wu, Alice Wu. FROcasting: A pilot of forecasting in the scientific review of focused research organizations. Experiment, maggio 2023. http://dx.doi.org/10.18258/51686.

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Passner, Jeffrey E. Using the Advanced Research Version of the Weather Research and Forecasting Model (WRF-ARW) to Forecast Turbulence at Small Scales. Fort Belvoir, VA: Defense Technical Information Center, settembre 2008. http://dx.doi.org/10.21236/ada487156.

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