Relevant bibliographies by topics / Forecasting accuracy

Academic literature on the topic 'Forecasting accuracy'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Forecasting accuracy"

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Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (August 21, 2016): 813. http://dx.doi.org/10.17722/ijme.v7i2.262.

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Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (August 31, 2016): 813–20. http://dx.doi.org/10.17722/ijme.v7i2.851.

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This article explores the impact of the different forecasting methods (FMs) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs), in Kenya. The objective of the study was to assess if the different forecasting methods have an influence on any of the aspects of measures of APF. APF, in manufacturing operations, is seldom derived accurately. However, LMFs tend to hire skilled forecasters, to a great extent, to ensure APF when preparing future budgets. The different types of forecasting techniques have been known to influence the behavior of operations resulting in the formulation of either accurate or inaccurate forecasts resulting in either adverse or favorable organizational performance. The study used the three known forecasting methods, objective, subjective and combined forecasting techniques against measures of APF, expected value, growth in market share, return on assets and return on sales. Regression analysis was used applying data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was evidence that APF is influenced by each of the forecasting methods in different ways.
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Doh, Joon-Chien. "Accuracy of Expenditure Forecasting." Asian Journal of Public Administration 11, no. 2 (December 1989): 200–215. http://dx.doi.org/10.1080/02598272.1989.10800221.

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Kimes, S. E. "Group Forecasting Accuracy in Hotels." Journal of the Operational Research Society 50, no. 11 (November 1999): 1104. http://dx.doi.org/10.2307/3010081.

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Kimes, S. E. "Group forecasting accuracy in hotels." Journal of the Operational Research Society 50, no. 11 (November 1999): 1104–10. http://dx.doi.org/10.1057/palgrave.jors.2600770.

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Makridakis, Spyros. "Forecasting accuracy and system complexity." RAIRO - Operations Research 29, no. 3 (1995): 259–83. http://dx.doi.org/10.1051/ro/1995290302591.

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Köchling, Gerrit, Philipp Schmidtke, and Peter N. Posch. "Volatility forecasting accuracy for Bitcoin." Economics Letters 191 (June 2020): 108836. http://dx.doi.org/10.1016/j.econlet.2019.108836.

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Chindia, E. W. "Forecasting Techniques, External Operating Environment and Accuracy of Performance Forecasting." Advances in Economics and Business 4, no. 8 (August 2016): 468–75. http://dx.doi.org/10.13189/aeb.2016.040809.

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Liu, Dunnan, Yu Hu, Yujie Xu, and Canbing Li. "“Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/897952.

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As an intermittent energy, wind power has the characteristics of randomness and uncontrollability. It is of great significance to improve the accuracy of wind power forecasting. Currently, most models for wind power forecasting are based on wind speed forecasting. However, it is stuck in a dilemma called “garbage in, garbage out,” which means it is difficult to improve the forecasting accuracy without improving the accuracy of input data such as the wind speed. In this paper, a new model based on cloud theory is proposed. It establishes a more accurate relational model between the wind power and wind speed, which has lots of catastrophe points. Then, combined with the trend during adjacent time and the laws of historical data, the forecasting value will be corrected by the theory of “section to point” correction. It significantly improves the stability of forecasting accuracy and reduces significant forecasting errors at some particular points. At last, by analyzing the data of generation power and historical wind speed in Inner Mongolia, China, it is proved that the proposed method can effectively improve the accuracy of wind speed forecasting.
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Geurts, Michael D., and H. Dennis Tolley. "Causal partitioning and sales forecasting accuracy." Journal of Statistical Computation and Simulation 37, no. 1-2 (October 1990): 1–12. http://dx.doi.org/10.1080/00949659008811289.

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Dissertations / Theses on the topic "Forecasting accuracy"

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Gunner, J. C. "A model of building price forecasting accuracy." Thesis, University of Salford, 1997. http://usir.salford.ac.uk/26702/.

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The purpose of this research was to derive a statistical model comprising the significant factors influencing the accuracy of a designer's price forecast and as an aid to providing a theoretical framework for further study. To this end data, comprising 181 building contract details, was collected from the Singapore office of an international firm of quantity surveyors over the period 1980 to 1991. Bivariate analysis showed a number of independent variables having significant effect on bias which was in general agreement with previous work in this domain. The research also identified a number of independent variables having significant effect on the consistency, or precision, of designers' building price forecasts. With information gleaned from bivariate results attempts were made to build a multivariate model which would explain a significant portion of the errors occurring in building price forecasts. The results of the models built were inconclusive because they failed to satisfy the assumptions inherent in ordinary least squares regression. The main failure in the models was in satisfying the assumption of homoscedasticity, that is, the conditional variances of the residuals are equal around the mean. Five recognised methodologies were applied to the data in attempts to remove heteroscedasticity but none were successful. A different approach to model building was then adopted and a tenable model was constructed which satisfied all of the regression assumptions and internal validity checks. The statistically significant model also revealed that the variable of Price Intensity was the sole underlying influence when tested against all other independentpage xiv variables in the data of this work and after partialling out the effect of all other independent variables. From this a Price Intensity theory of accuracy is developed and a further review of the previous work in this field suggests that this may be of universal application.
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Lindström, Markus. "Forecasting day-ahead electricity prices in Sweden : Has the forecasting accuracy decreased?" Thesis, Umeå universitet, Nationalekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184649.

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Sweden is currently transitioning towards having 100% electricity generation from renewable energy sources by 2040. To reach this goal, Sweden will ramp up the generation from wind power while simultaneously phasing out nuclear power. Replacing nuclear power with an intermittent production source such as wind power has been proven to increase the variability of electricity prices. The purpose of this study has been to investigate if the increasing electricity generation through wind power in Sweden has decreased the accuracy of price forecasts provided by ARIMA models. Using an automated algorithm in R, optimal ARIMA models were chosen to forecast on-peak and off-peak hours for both winter and summer periods in 2015. These forecasts were then compared to forecasts provided by ARIMA models calibrated on data from 2020. The results from the empirical analysis showed that the overall forecast errors are twice as large for the 2020 forecasts indicating that increasing electricity generation from wind power has decreased the forecasting accuracy of price-only statistical models.
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Zbib, Imad J. (Imad Jamil). "Sales Forecasting Accuracy Over Time: An Empirical Investigation." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332526/.

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This study investigated forecasting accuracy over time. Several quantitative and qualitative forecasting models were tested and a number of combinational methods was investigated. Six time series methods, one causal model, and one subjective technique were compared in this study. Six combinational forecasts were generated and compared to individual forecasts. A combining technique was developed. Thirty data sets, obtained from a market leader in the cosmetics industry, were used to forecast sales. All series represent monthly sales from January 1985 to December 1989. Gross sales forecasts from January 1988 to June 1989 were generated by the company using econometric models. All data sets exhibited seasonality and trend.
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SESKAUSKIS, ZYGIMANTAS, and ROKAS NARKEVICIUS. "Sales forecasting management." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-10685.

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The purpose of this research is to investigate current company business process from sales forecasting perspective and provide potential improvements of how to deal with unstable market demand and increase overall precision of forecasting. The problem which company face is an unstable market demand and not enough precision in sales forecasting process. Therefore the research questions are:  How current forecasting process can be improved?  What methods, can be implemented in order to increase the precision of forecasting? Study can be described as an action research using an abductive approach supported by combination of quantitative and qualitative analysis practices. In order to achieve high degree of reliability the study was based on verified scientific literature and data collected from the case company while collaborating with company’s COO. Research exposed the current forecasting process of the case company. Different forecasting methods were chosen according to the existing circumstances and analyzed in order to figure out which could be implemented in order to increase forecasting precision and forecasting as a whole. Simple exponential smoothing showed the most promising accuracy results, which were measured by applying MAD, MSE and MAPE measurement techniques. Moreover, trend line analysis was applied as well, as a supplementary method. For the reason that the case company presents new products to the market limited amount of historical data was available. Therefore simple exponential smoothing technique did not show accurate results as desired. However, suggested methods can be applied for testing and learning purposes, supported by currently applied qualitative methods.
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Novela, George. "Testing maquiladora forecast accuracy." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Karimi, Arizo. "VARs and ECMs in forecasting – a comparative study of the accuracy in forecasting Swedish exports." Thesis, Uppsala University, Department of Economics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9223.

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In this paper, the forecast performance of an unrestricted Vector Autoregressive (VAR) model was compared against the forecast accuracy of a Vector error correction (VECM) model when computing out-of-sample forecasts for Swedish exports. The co-integrating relation used to estimate the error correction specification was based upon an economic theory for international trade suggesting that a long run equilibrium relation among the variables included in an export demand equation should exist. The results obtained provide evidence of a long run equilibrium relationship between the Swedish export volume and its main determinants. The models were estimated for manufactured goods using quarterly data for the period 1975-1999 and once estimated, the models were used to compute out-of-sample forecasts up to four-, eight- and twelve-quarters ahead for the Swedish export volume using both multi-step and one-step ahead forecast techniques. The main results suggest that the differences in forecasting ability between the two models are small, however according to the relevant evaluation criteria the unrestricted VAR model in general yields somewhat better forecast than the VECM model when forecasting Swedish exports over the chosen forecast horizons.

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Eroglu, Cuneyt. "An investigation of accuracy, learning and biases in judgmental adjustments of statistical forecasts." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1150398313.

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Bilodeau, Bernard. "Accuracy of a truncated barotropic spectral model : numerical versus analytical solutions." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66037.

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Yongtao, Yu. "Exchange rate forecasting model comparison: A case study in North Europe." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154948.

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In the past, a lot of studies about the comparison of exchange rate forecasting models have been carried out. Most of these studies have a similar result which is the random walk model has the best forecasting performance. In this thesis, I want to find a model to beat the random walk model in forecasting the exchange rate. In my study, the vector autoregressive model (VAR), restricted vector autoregressive model (RVAR), vector error correction model (VEC), Bayesian vector autoregressive model are employed in the analysis. These multivariable time series models are compared with the random walk model by evaluating the forecasting accuracy of the exchange rate for three North European countries both in short-term and long-term. For short-term, it can be concluded that the random walk model has the best forecasting accuracy. However, for long-term, the random walk model is beaten. The equal accuracy test proves this phenomenon really exists.
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Orrebrant, Richard, and Adam Hill. "Increasing sales forecast accuracy with technique adoption in the forecasting process." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Industriell organisation och produktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-24038.

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Abstract   Purpose - The purpose with this thesis is to investigate how to increase sales forecast accuracy.   Methodology – To fulfil the purpose a case study was conducted. To collect data from the case study the authors performed interviews and gathered documents. The empirical data was then analysed and compared with the theoretical framework.   Result – The result shows that inaccuracies in forecasts are not necessarily because of the forecasting technique but can be a result from an unorganized forecasting process and having an inefficient information flow. The result further shows that it is not only important to review the information flow within the company but in the supply chain as whole to improve a forecast’s accuracy. The result also shows that time series can generate more accurate sales forecasts compared to only using qualitative techniques. It is, however, necessary to use a qualitative technique when creating time series. Time series only take time and sales history into account when forecasting, expertise regarding consumer behaviour, promotion activity, and so on, is therefore needed. It is also crucial to use qualitative techniques when selecting time series technique to achieve higher sales forecast accuracy. Personal expertise and experience are needed to identify if there is enough sales history, how much the sales are fluctuating, and if there will be any seasonality in the forecast. If companies gain knowledge about the benefits from each technique the combination can improve the forecasting process and increase the accuracy of the sales forecast.   Conclusions – This thesis, with support from a case study, shows how time series and qualitative techniques can be combined to achieve higher accuracy. Companies that want to achieve higher accuracy need to know how the different techniques work and what is needed to take into account when creating a sales forecast. It is also important to have knowledge about the benefits of a well-designed forecasting process, and to do that, improving the information flow both within the company and the supply chain is a necessity.      Research limitations – Because there are several different techniques to apply when creating a sales forecast, the authors could have involved more techniques in the investigation. The thesis work could also have used multiple case study objects to increase the external validity of the thesis.
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Books on the topic "Forecasting accuracy"

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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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Fund, International Monetary. Forecasting accuracy of crude oil futures prices. Washington, D.C: International Monetary Fund, 1991.

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Fildes, Robert. Accuracy gains through individual univariate forecasting: Model selection. Manchester: Manchester Business School, 1986.

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Population projections and their accuracy. Delhi: B.R. Pub. Corp., 2000.

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Makridakis, Spyros. "Metaforecasting: Ways of improving forecasting. Accuracy and Usefulness". Fontainbleau: INSEAD, 1986.

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Grissmer, David W. The accuracy of simple enlisted force forecasts. Santa Monica, CA (P.O. Box 2138, Santa Monica 90406-2138): Rand, 1985.

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Berger, Helge. Forecasting ECB monetary policy: Accuracy is (still) a matter of geography. [Washington, D.C.]: International Monetary Fund, European Dept., 2006.

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Fildes, Robert. The impact of empirical accuracy studies on time series analysis and forecasting. Fontainbleau: INSEAD, 1993.

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National Research Council (U.S.). Committee for a Workshop on Weather Forecasting Accuracy for FAA Air Traffic Control. Weather forecasting accuracy for FAA traffic flow management: A workshop report. Washington, D.C: National Academies Press, 2003.

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Stephen, MacDonald. The accuracy of USDA's export forecasts. Washington, DC: U.S. Dept. of Agriculture, Economic Research Service, Commodity Economics Division, 1992.

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Book chapters on the topic "Forecasting accuracy"

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Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Accuracy." In Economic Forecasting, 235–50. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_11.

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Moosa, Imad A. "Measuring Forecasting Accuracy." In Exchange Rate Forecasting, 316–36. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230379008_10.

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Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Risks and Accuracy." In Economic Forecasting and Policy, 265–308. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230306448_8.

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Dhrymes, Phoebus. "Forecasting: Accuracy and Evaluation." In Introductory Econometrics, 477–526. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65916-9_8.

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Moosa, Imad A., and Kelly Burns. "Alternative Measures of Forecasting Accuracy." In Demystifying the Meese-Rogoff Puzzle, 44–62. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137452481_4.

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Guerard, John B. "Forecasting: Its Purpose and Accuracy." In Introduction to Financial Forecasting in Investment Analysis, 1–18. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5239-3_1.

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Booth, Heather. "Coherent Mortality Forecasting with Standards: Low Mortality Serves as a Guide." In Developments in Demographic Forecasting, 153–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_8.

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Abstract Mortality forecasts are an important component of population forecasting and are central to the estimation of longevity risk in actuarial practice. Planning by the state for health and aged care services and by individuals for retirement and later life depends on accurate mortality forecasts. The overall accuracy or performance of mortality forecasting has improved since Lee and Carter (1992) introduced stochastic forecasting of mortality to the demographic community, and further improvements can undoubtedly be made.
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Williams, Daniel, and Thad Calabrese. "Current Midyear Municipal Budget Forecast Accuracy." In The Palgrave Handbook of Government Budget Forecasting, 257–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18195-6_13.

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Wilkie, Mary E., and Andrew C. Pollock. "Currency Forecasting: An Investigation Into Probability Judgement Accuracy." In Financial Modelling, 354–64. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-86706-4_22.

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Hassan, Saima, Abbas Khosravi, Jafreezal Jaafar, and Samir B. Belhaouari. "Load Forecasting Accuracy through Combination of Trimmed Forecasts." In Neural Information Processing, 152–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34475-6_19.

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Conference papers on the topic "Forecasting accuracy"

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McGarry, Michael P., Patrick Seeling, Rami Haddad, and Jesus Hernandez. "Accuracy of video frame size forecasting." In 2012 IEEE International Conference on Electro/Information Technology (EIT 2012). IEEE, 2012. http://dx.doi.org/10.1109/eit.2012.6220754.

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Zaunseder, Elaine, Larissa Müller, and Sven Blankenburg. "High Accuracy Forecasting with Limited Input Data." In the Ninth International Symposium. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3287921.3287936.

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Abu Ali, Najah A., and Mervat Abu-Elkheir. "Regression forecasting model to improve localization accuracy." In 2015 International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2015. http://dx.doi.org/10.1109/iwcmc.2015.7289262.

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"Examination of property forecasting models - accuracy and its improvement through combination forecasting." In 19th Annual European Real Estate Society Conference: ERES Conference 2012. ERES, 2012. http://dx.doi.org/10.15396/eres2012_082.

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Jennings, Connor, Dazhong Wu, and Janis Terpenny. "Forecasting Obsolescence Risk Using Machine Learning." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8625.

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With rapid innovation in the electronics industry, product obsolescence forecasting has become increasingly important. More accurate obsolescence forecasting would have cost reduction effects in product design and part procurement over a product’s lifetime. Currently many obsolescence forecasting methods require manual input or perform market analysis on a part by part basis; practices that are not feasible for large bill of materials. In response, this paper introduces an obsolescence forecasting framework that is capable of being scaled to meet industry needs while remaining highly accurate. The framework utilizes machine learning to classify parts as active, in production, or obsolete and discontinued. This classification and labeling of parts can be useful in the design stage in part selection and during inventory management with evaluating the chance that suppliers might stop production. A case study utilizing the proposed framework is presented to demonstrate and validate the improved accuracy of obsolescence risk forecasting. As shown, the framework correctly identified active and obsolete products with an accuracy as high as 98.3%.
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Peralta, Juan, German Gutierrez, and Araceli Sanchis. "Shuffle design to improve time series forecasting accuracy." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983019.

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"The Accuracy of Property Forecasting in the UK." In 10th European Real Estate Society Conference: ERES Conference 2003. ERES, 2003. http://dx.doi.org/10.15396/eres2003_229.

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Bramm, Andrey, and Alexandra Khalyasmaa. "Forecasting Accuracy Improvement of Solar Power Plant's Generation." In 2021 XVIII International Scientific Technical Conference Alternating Current Electric Drives (ACED). IEEE, 2021. http://dx.doi.org/10.1109/aced50605.2021.9462283.

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Baker, Kathleen M., and Ricardo Nogueira. "Climate variability and the daily agroecosystem forecasting accuracy plateau." In 2012 First International Conference on Agro-Geoinformatics. IEEE, 2012. http://dx.doi.org/10.1109/agro-geoinformatics.2012.6311677.

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Hassan, S., A. Khosravi, and J. Jaafar. "Improving load forecasting accuracy through combination of best forecasts." In 2012 IEEE International Conference on Power System Technology (POWERCON 2012). IEEE, 2012. http://dx.doi.org/10.1109/powercon.2012.6401332.

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Reports on the topic "Forecasting accuracy"

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Cook, Steve. Directional Forecasting, Forecasting Accuracy and Making Profits. Bristol, UK: The Economics Network, September 2014. http://dx.doi.org/10.53593/n2703a.

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Hafer, R. W. Forecasting Economic Activity: Comparing the Accuracy of Survey and Time Series Predictions. Federal Reserve Bank of St. Louis, 1985. http://dx.doi.org/10.20955/wp.1985.012.

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Cogan, James L. Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS). Fort Belvoir, VA: Defense Technical Information Center, September 2016. http://dx.doi.org/10.21236/ad1016607.

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Sofia, Sabatino. Development of a System for Accurate Forecasting of Solar Activity. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada295327.

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Sofia, Sabatino. Development of a System for Accurate Forecasting of Solar Activity. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada262450.

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Freeman, Rahsul J. Mitigating the Erratic Behavior of the Transportation Working Capital Fund Through Accurate Forecasting. Fort Belvoir, VA: Defense Technical Information Center, June 2015. http://dx.doi.org/10.21236/ada619562.

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