Relevant bibliographies by topics / Forecasting strategies

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Forecasting strategies'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Forecasting strategies"

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Bar-On, Raphael R. "Forecasting Tourism Demand: Methods and Strategies." Annals of Tourism Research 30, no. 3 (July 2003): 754–56. http://dx.doi.org/10.1016/s0160-7383(03)00051-3.

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Junk, Constantin, Luca Delle Monache, Stefano Alessandrini, Guido Cervone, and Lueder von Bremen. "Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble." Meteorologische Zeitschrift 24, no. 4 (July 21, 2015): 361–79. http://dx.doi.org/10.1127/metz/2015/0659.

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Rožanec, Jože M., Blaž Kažič, Maja Škrjanc, Blaž Fortuna, and Dunja Mladenić. "Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies." Applied Sciences 11, no. 15 (July 23, 2021): 6787. http://dx.doi.org/10.3390/app11156787.

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Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.
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Kaiser, Mark J. "Multiple well lease decomposition and forecasting strategies." Journal of Petroleum Science and Engineering 116 (April 2014): 59–71. http://dx.doi.org/10.1016/j.petrol.2014.02.016.

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Savio, Nicolas, and Konstantinos Nikolopoulos. "Forecasting the Effectiveness of Policy Implementation Strategies." International Journal of Public Administration 33, no. 2 (January 13, 2010): 88–97. http://dx.doi.org/10.1080/01900690903241765.

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SCHWARZKOPF, ALBERT B., RICHARD J. TERSINE, and JOHN S. MORRIS. "Top-down versus bottom-up forecasting strategies." International Journal of Production Research 26, no. 11 (November 1988): 1833–43. http://dx.doi.org/10.1080/00207548808947995.

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Moniz, Nuno, Paula Branco, and Luís Torgo. "Resampling strategies for imbalanced time series forecasting." International Journal of Data Science and Analytics 3, no. 3 (February 16, 2017): 161–81. http://dx.doi.org/10.1007/s41060-017-0044-3.

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Hadri, Sarah, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, and Mohamed El Arroussi. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings." Energies 14, no. 18 (September 15, 2021): 5831. http://dx.doi.org/10.3390/en14185831.

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In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.
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Bakri, Rizal, Umar Data, and Andika Saputra. "Marketing Research : The Application of Auto Sales Forecasting Software to Optimize Product Marketing Strategies." Journal of Applied Science, Engineering, Technology, and Education 1, no. 1 (November 10, 2019): 6–12. http://dx.doi.org/10.35877/454ri.asci1124.

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The aims of this study is to apply the Auto Sales Forecasting software to predict sales transaction data. The Auto Sales Forecasting software consists of two main features namely descriptive analysis and forcasting features along with its visualization. Forecasting methods contained in the Auto Sales Forecasting application are forecasting methods of Simple Moving Average, Robust Exponantial Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. The Auto Sales Forecasting software can intelligently choose the best forecasting method based on RMSE values. The results showed that the Auto Sales Forecasting software successfully analyzed the sales transaction data. From the analysis it was found that there were 43 types of products produced and sold by the Futry Bakery & Cake Store. Three of them are the types of products that are most in demand by consumers, namely Sweet Bread, Maros Bread, and Traditional Cakes 3500. The best selling product type, Sweet Bread, is used to build forecasting models. The best forecasting method is the Robust Exponential Smoothing method with the smallest RMSE value of 0.83 on the variable number of sold out products. Forecasting results using the Robust Exponantial Smoothing method show that the average number of products to sell for the next seven days ranges from 116 products with a certain confidence interval value.
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Mellers, Barbara, Lyle Ungar, Jonathan Baron, Jaime Ramos, Burcu Gurcay, Katrina Fincher, Sydney E. Scott, et al. "Psychological Strategies for Winning a Geopolitical Forecasting Tournament." Psychological Science 25, no. 5 (March 21, 2014): 1106–15. http://dx.doi.org/10.1177/0956797614524255.

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Dissertations / Theses on the topic "Forecasting strategies"

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Savio, Nicolas Domingo. "Forecasting the effectiveness of policy implementation strategies." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/forecasting-the-effectiveness-of-policy-implementation-strategies(7d560826-a9bf-4223-8658-02240934ade9).html.

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An important stage in the policy process involves deciding what strategy is to be adopted for implementation so that the objectives of the policy are met in the best way possible. A Policy Implementation Strategy (PIS) adopts a broad view of implementation, which is argued to transcend formulation and decision-making, thereby offering a more realistic view of the policy process. Governmental decision-makers are often faced with having to choose one PIS amongst several possible alternatives, at varying cost levels. In order to aid in such a decision-making process, PIS effectiveness forecasts are proposed as a decision-support tool.Current methods for such a purpose are found to include ex-ante evaluative techniques such as Impact Assessment (IA) and Cost-Benefit Analysis (CBA). However, these approaches are often resource-intensive and such an investment is not always rewarded with accurate predictions. Hence, a judgmental forecasting approach for making PIS effectiveness predictions is proposed as a means for screening the different PIS under contention to provide a shortlist of candidates with particular potential. The selected few can then be further analysed via the quantitative evaluative techniques such as IA and CBA. Judgmental approaches to forecasting are considered ideal for such a role because they are relatively quick and inexpensive to implement. More specifically, a structured analogies approach is proposed as information about analogous PIS is believed to be useful for such a purpose.The proposed structured analogies approach is tested over a series of experiments and the evidence suggests that a structured analogies approach is more accurate when compared to unaided judgment and the more support given to the expert the better. Furthermore, experts were seen to produce considerably more accurate predictions than non-experts. Level of experience and number of analogies recalled did not seem to affect accuracy. The expert forecasts were also comparable to those produced by governments. The thesis concludes with suggestions for future research in the area.
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Li, Mingfei. "Strategies in repeated games." Diss., Connect to online resource - MSU authorized users, 2008.

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Eguasa, Uyi Harrison. "Strategies to Improve Data Quality for Forecasting Repairable Spare Parts." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/3155.

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Poor input data quality used in repairable spare parts forecasting by aerospace small and midsize enterprises (SME) suppliers results in poor inventory practices that manifest into higher costs and critical supply shortage risks. Guided by the data quality management (DQM) theory as the conceptual framework, the purpose of this exploratory multiple case study was to identify the key strategies that the aerospace SME repairable spares suppliers use to maximize their input data quality used in forecasting repairable spare parts. The multiple case study comprised of a census sample of 6 forecasting business leaders from aerospace SME repairable spares suppliers located in the states of Florida and Kansas. The sample was collected via semistructured interviews and supporting documentation from the consenting participants and organizational websites. Eight core themes emanated from the application of the content data analysis process coupled with methodological triangulation. These themes were labeled as establish data governance, identify quality forecast input data sources, develop a sustainable relationship and collaboration with customers and vendors, utilize a strategic data quality system, conduct continuous input data quality analysis, identify input data quality measures, incorporate continuous improvement initiatives, and engage in data quality training and education. Of the 8 core themes, 6 aligned to the DQM theory's conceptual constructs while 2 surfaced as outliers. The key implication of the research toward positive social change may include the increased situational awareness for SME forecasting business leaders to focus on enhancing business practices for input data quality to forecast repairable spare parts to attain sustainable profits.
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Ben, Taieb Souhaib. "Machine learning strategies for multi-step-ahead time series forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209234.

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How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series.

Historically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.

The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.

Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.

In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.

Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.

We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.

Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.


Doctorat en sciences, Spécialisation Informatique
info:eu-repo/semantics/nonPublished

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Crespo, Cuaresma Jesus, Ines Fortin, and Jaroslava Hlouskova. "Exchange rate forecasting and the performance of currency portfolios." Wiley, 2018. http://dx.doi.org/10.1002/for.2518.

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We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error-based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state-of-the-art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies.
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Aronsson, Henrik. "Modeling strategies using predictive analytics : Forecasting future sales and churn management." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167130.

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This project was carried out for a company named Attollo, a consulting firm specialized in Business Intelligence and Corporate Performance Management. The project aims to explore a new area for Attollo, predictive analytics, which is then applied to Klarna, a client of Attollo. Attollo has a partnership with IBM, which sells services for predictive analytics. The tool that this project is carried out with, is a software from IBM: SPSS Modeler. Five different examples are given of what and how the predictive work that was carried out at Klarna consisted of. From these examples, the different predictive models' functionality are described. The result of this project demonstrates, by using predictive analytics, how predictive models can be created. The conclusion is that predictive analytics enables companies to understand their customers better and hence make better decisions.
Detta projekt har utforts tillsammans med ett foretag som heter Attollo, en konsultfirma som ar specialiserade inom Business Intelligence & Coporate Performance Management. Projektet grundar sig pa att Attollo ville utforska ett nytt omrade, prediktiv analys, som sedan applicerades pa Klarna, en kund till Attollo. Attollo har ett partnerskap med IBM, som saljer tjanster for prediktiv analys. Verktyget som detta projekt utforts med, ar en mjukvara fran IBM: SPSS Modeler. Fem olika exempel beskriver det prediktiva arbetet som utfordes vid Klarna. Fran dessa exempel beskrivs ocksa de olika prediktiva modellernas funktionalitet. Resultatet av detta projekt visar hur man genom prediktiv analys kan skapa prediktiva modeller. Slutsatsen ar att prediktiv analys ger foretag storre mojlighet att forsta sina kunder och darav kunna gora battre beslut.
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Watkins, Arica. "Successful Demand Forecasting Modeling Strategies for Increasing Small Retail Medical Supply Profitability." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7576.

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The lack of effective demand forecasting strategies can result in imprecise inventory replenishment, inventory overstock, and unused inventory. The purpose of this single case study was to explore successful demand forecasting strategies that leaders of a small, retail, medical supply business used to increase profitability. The conceptual framework for this study was Winters's forecasting demand approach. Data were collected from semistructured, face-to-face interviews with 8 business leaders of a private, small, retail, medical supply business in the southeastern United States and the review of company artifacts. Yin's 5-step qualitative data analysis process of compiling, disassembling, reassembling, interpreting, and concluding was applied. Key themes that emerged from data analysis included understanding sales trends, inventory management with pricing, and seasonality. The findings of this study might contribute to positive social change by encouraging leaders of medical supply businesses to apply demand forecasting strategies that may lead to benefits for medically underserved citizens in need of accessible and abundant medical supplies.
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Hoover, Michael G. "Corn storage marketing strategies for Virginia." Thesis, This resource online, 1997. http://scholar.lib.vt.edu/theses/available/etd-08222008-063143/.

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Beckman, Charles V. "Multiple year pricing strategies for corn and soybeans using cash, futures, and options contracts." Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06162009-063615/.

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Shao, Renyuan. "The Design and Evaluation of Price Risk Management Strategies in the U.S. Hog Industry." The Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1051933573.

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Books on the topic "Forecasting strategies"

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C, Frechtling Douglas, ed. Forecasting tourism demand: Methods and strategies. Oxford: Butterworth-Heinemann, 2001.

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Schaap, Charles B. ADXcellence: Power trend strategies. Las Vegas, Nev: StockMarketStore, 2006.

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Anthony, Dunning, ed. Computer strategies, 1990-9: Technologies, costs, markets. Chichester [West Sussex]: Wiley, 1987.

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Exchange-rate determination: Models and strategies for exchange rate forecasting. New York: McGraw-Hill, 2003.

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Boselly, S. Edward. Weather forecasting strategies for city and county road maintenance operations. Olympia, WA: Washington State Dept. of Transportation, 1990.

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Binary options: Strategies and tactics. Hoboken, NJ: Wiley, 2011.

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Yeoh, Michael. Vision & leadership: Values and strategies towards vision 2020. Petaling Jaya, Selangor Darul Ehsan, Malaysia: Pelanduk Publications, 1995.

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Pesaran, Hashem. The use of recursive model selection strategies in forecasting stock returns. Cambridge: University of Cambridge, Department of Applied Economics, 1994.

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Forecasting for real estate wealth: Strategies for outperforming any housing market. Hoboken, N.J: Wiley, 2008.

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China's economic development strategies for the 21st century. Westport, Conn: Quorum Books, 1997.

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Book chapters on the topic "Forecasting strategies"

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Cowpertwait, Paul S. P., and Andrew V. Metcalfe. "Forecasting Strategies." In Introductory Time Series with R, 45–66. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-88698-5_3.

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Knox, John A., Alan W. Black, Jared A. Rackley, Emily N. Wilson, Jeremiah S. Grant, Stephanie P. Phelps, David S. Nevius, and Corey B. Dunn. "Automated Turbulence Forecasting Strategies." In Aviation Turbulence, 243–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23630-8_12.

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Antonov, A. G., and V. E. Kambulin. "Forecasting seasonal dynamics of the Asiatic migratory locust using the Locusta migratoria migratoria — Phragmites australis forecasting system." In New Strategies in Locust Control, 81–89. Basel: Birkhäuser Basel, 1997. http://dx.doi.org/10.1007/978-3-0348-9202-5_11.

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Bontempi, Gianluca, Souhaib Ben Taieb, and Yann-Aël Le Borgne. "Machine Learning Strategies for Time Series Forecasting." In Business Intelligence, 62–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36318-4_3.

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Fischer, Ulrike, and Wolfgang Lehner. "Transparent Forecasting Strategies in Database Management Systems." In Business Intelligence, 150–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05461-2_5.

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Cressman, K. "SWARMS: A geographic information system for desert locust forecasting." In New Strategies in Locust Control, 27–35. Basel: Birkhäuser Basel, 1997. http://dx.doi.org/10.1007/978-3-0348-9202-5_4.

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Tuteja, Narendra Kumar, Senlin Zhou, Julien Lerat, Q. J. Wang, Daehyok Shin, and David E. Robertson. "Overview of Communication Strategies for Uncertainty in Hydrological Forecasting in Australia." In Handbook of Hydrometeorological Ensemble Forecasting, 1–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-642-40457-3_73-1.

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Tuteja, Narendra Kumar, Senlin Zhou, Julien Lerat, Q. J. Wang, Daehyok Shin, and David E. Robertson. "Overview of Communication Strategies for Uncertainty in Hydrological Forecasting in Australia." In Handbook of Hydrometeorological Ensemble Forecasting, 1161–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-39925-1_73.

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Cressman, K. "Results and recommendations of the working group Forecasting and modelling." In New Strategies in Locust Control, 99–101. Basel: Birkhäuser Basel, 1997. http://dx.doi.org/10.1007/978-3-0348-9202-5_14.

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Sheikh, A. K., M. Younas, and A. Raouf. "Reliability Based Spare Parts Forecasting and Procurement Strategies." In Maintenance, Modeling and Optimization, 81–110. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4329-9_4.

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Conference papers on the topic "Forecasting strategies"

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Tetarenko, Alex, Harriet Parsons, Sarah F. Graves, and Jessica Dempsey. "Automated project completion forecasting." In Observatory Operations: Strategies, Processes, and Systems VIII, edited by Chris R. Benn, Robert L. Seaman, and David S. Adler. SPIE, 2020. http://dx.doi.org/10.1117/12.2561634.

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Feng, Cong, and Jie Zhang. "Short-Term Load Forecasting With Different Aggregation Strategies." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86084.

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Effective short-term load forecasting (STLF) plays an important role in demand-side management and power system operations. In this paper, STLF with three aggregation strategies are developed, which are information aggregation (IA), model aggregation (MA), and hierarchy aggregation (HA). The IA, MA, and HA strategies aggregate inputs, models, and forecasts, respectively, at different stages in the forecasting process. To verify the effectiveness of the three aggregation STLF, a set of 10 models based on 4 machine learning algorithms, i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest, are developed in each aggregation group to predict 1-hour-ahead load. Case studies based on 2-year of university campus data with 13 individual buildings showed that: (a) STLF with three aggregation strategies improves forecasting accuracy, compared with benchmarks without aggregation; (b) STLF-IA consistently presents superior behavior than STLF based on weather data and STLF based on individual load data; (c) MA reduces the occurrence of unsatisfactory single-algorithm STLF models, therefore enhancing the STLF robustness; (d) STLF-HA produces the most accurate forecasts in distinctive load pattern scenarios due to calendar effects.
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Oliveira, Mariana, Nuno Moniz, Luis Torgo, and Vitor Santos Costa. "Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting." In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2019. http://dx.doi.org/10.1109/dsaa.2019.00024.

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Sun, Chao, Xiaosong Hu, Scott J. Moura, and Fengchun Sun. "Comparison of Velocity Forecasting Strategies for Predictive Control in HEVs." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6031.

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The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or on-board sensor information is required. A comparative study is conducted between the ANN-based method and two other velocity predictors: generalized exponentially varying and Markov-chain models. The sensitivity of the prediction precision and computational cost on tuning parameters in examined for each forecasting strategy. Validation results show that the ANN-based velocity predictor exhibits the best overall performance with respect to minimizing fuel consumption.
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Voynarenko, Mykhaylo, Alla Cherep, Olga Gonchar, Alexander Cherep, Denis Krylov, and Lyudmila Oleynikova. "Information Provision For Forecasting Strategies Innovative Activities Of Enterprises." In 2019 9th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2019. http://dx.doi.org/10.1109/acitt.2019.8780030.

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Cao, Yan, Zhong Jun Zhang, and Chi Zhou. "Data Processing Strategies in Short Term Electric Load Forecasting." In 2012 International Conference on Computer Science and Service System (CSSS). IEEE, 2012. http://dx.doi.org/10.1109/csss.2012.51.

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Rodriguez, Hector, Manuel Medrano, Luis Morales Rosales, Gloria Peralta Penunuri, and Juan Jose Flores. "Multi-step forecasting strategies for wind speed time series." In 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2020. http://dx.doi.org/10.1109/ropec50909.2020.9258743.

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8

Sansa, Ines, Sihem Missaoui, Zina Boussada, Najiba Mrabet Bellaaj, Emad M. Ahmed, and Mouhamed Orabi. "PV power forecasting using different Artificial Neural Networks strategies." In 2014 International Conference on Green Energy. IEEE, 2014. http://dx.doi.org/10.1109/icge.2014.6835397.

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Ozdoeva, Alina, and Denis Seleznev. "Tools for innovation strategies." In International Conference "Computing for Physics and Technology - CPT2020". Bryansk State Technical University, 2020. http://dx.doi.org/10.30987/conferencearticle_5fce2771a37ca5.74416745.

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Abstract:
The current article is devoted to search tools for determining the optimal solution and forming the optimal company strategy for small innovative companies in the business innovation environment of the oil and gas complex. The main area of research in the article is the reasons for the difficulties of innovative Russian entrepreneurship and its entry into the domestic market and work in this market. We also consider tools such as SWIFT-analysis of assessment and forecasting of the company's performance, the portfolio model of BCG (Boston consulting group), a multi-factor matrix for selecting strategies for the most effective planning of the company's activities, as an improved version of the Arthur D. Little model. At the same time, the study revealed that a wider range of project and strategic opportunities for planning and managing a company is formed by the production and economic matrix using SWOT analysis. Thus, based on this study, the following recommendations were formulated for beginning entrepreneurs and developers in the field of innovation: take into account and apply the strategy for small innovative enterprises according to the SWOT analysis for monitoring and forecasting upcoming events (production or economic); use marketing research tools, as well as forms for planning a product plan for the life of the company; take into account that the forecast should be based on strategic analysis, using the optimal method for specific goals, and be the starting point for developing new models and business development plans.
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Maciel, Leandro, Fernando Gomide, David Santos, and Rosangela Ballini. "Exchange rate forecasting using echo state networks for trading strategies." In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2014. http://dx.doi.org/10.1109/cifer.2014.6924052.

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Reports on the topic "Forecasting strategies"

1

Cho, Yonghee. Exploring Technology Forecasting and Its Implications for Strategic Technology Planning. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6108.

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