Relevant bibliographies by topics / Forecasting accuracy

Contents

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

Academic literature on the topic 'Forecasting accuracy'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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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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Sunny, Mathew, and Krishnan S. Ram. "Study of Sales Forecasting Accuracy using ARIMA Model." MERC Global's International Journal of Management, 8, no. 2 (April 30, 2020): 40–46. https://doi.org/10.35620/IJM.2020.8201.

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Forecasting is the program of action that entails an objective study of the past, present and future, to best estimate what that future holds in the way of sales for any given product or firm. One of the earliest recorded attempts at quantitative forecasting was that of John H. Patterson for the National Cash Register Company in 1887. Forecasting models have been widely investigated by researchers and practitioners. Here to understand the accuracy of forecasting, a study was conducted at Tata Global Beverages Limited (TGBL). The sample size for the study consists of the sales for the tea brand of TGBL during the last five years (April 2013 – March 2018). Holt Linear Trend, Holt Winter Model and Auto-Regressive Integrated Moving Average (ARIMA) model were used for forecasting the 2019 year sales data. Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used for finding accuracy and bar graphs are used for interpreting the results. The output of the study points out the variation between the forecasted sales and actual sales resulting in finding out the accuracy of a different model.
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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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Ramesh Kumar, Sowmya. "Accuracy vs. Interpretability: Balancing Trade - Offs in Forecasting Models." International Journal of Science and Research (IJSR) 10, no. 3 (March 5, 2021): 1964–66. http://dx.doi.org/10.21275/sr24213015550.

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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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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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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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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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Hitam, Nor Azizah, and Amelia Ritahani Ismail. "Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (September 1, 2018): 1121. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp1121-1128.

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Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.
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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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<p>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.</p>
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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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Makridakis, Spyros. "Metaforecasting: Ways of improving forecasting. Accuracy and Usefulness". Fontainbleau: INSEAD, 1986.

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National Renewable Energy Laboratory (U.S.) and International Workshop on the Integration of Solar Power into Power Systems (3rd : 2013 : London, England), eds. Metrics for evaluating the accuracy of solar power forecasting. Golden, CO: National Renewable Energy Laboratory, 2013.

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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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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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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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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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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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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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Conference papers on the topic "Forecasting accuracy"

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Mascarenhas, Maria Margarida, Mikael Amelin, and Hussain Kazmi. "Bridging Accuracy and Explainability in Electricity Price Forecasting." In 2024 20th International Conference on the European Energy Market (EEM), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/eem60825.2024.10608857.

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Liu, Ping. "Demand Forecasting: Cross-Functional, Cross-Disciplinary Analytics." In Vertical Flight Society 73rd Annual Forum & Technology Display, 1–6. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12219.

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Accurate material demand forecasting can lead to significant cost savings, greater competitiveness and improved customer satisfaction. However, more often than not, demand forecasting as a business function is carried out poorly, with forecast accuracy often not significantly better than the naïve forecast. To appropriately address these concerns and satisfy overall business objectives, it's increasingly important to have a holistic strategy to improve demand forecast accuracy through a well thought-out enterprise data strategy, applications of advanced forecasting methods as well as synchronized cross functional business processes. This paper describes data types that are essential to demand forecasting, investigates advanced analytics methods such as ARIMA and survival analysis and discusses the application of these methods for the purpose of fleet sustainment demand forecasting. Lastly, this paper addresses the business process needed to continuously monitor and improve forecast performance.
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Pisaneschi, Giulio, Davide Fioriti, Sandra Banda, Anne Wacera Wambugu, Izael Da Silva, and Davide Poli. "Electricity Forecasting in Kenyan Off-grid Microgrid: Forecasting Accuracy Versus Multi-Year Load Growth." In 2024 IEEE International Humanitarian Technologies Conference (IHTC), 1–6. IEEE, 2024. https://doi.org/10.1109/ihtc61819.2024.10855058.

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Gopala Krishna, K. Sai, B. Vikranth, Yashasree Jambavathi, and Rakesh Kumar Godi. "Deep Learning Models for High Accuracy Stock Price Forecasting." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545104.

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Chauhan, Shanvi. "Machine Learning Models for Loan Default Forecasting: Accuracy Comparison." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES), 1–5. IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877523.

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Boltaikhanova, Tomiris, Fares A. Dael, Ibraheem Shayea, and Rzayeva Leila. "Data-Driven Strategies for Improving Railway Ticket Demand Forecasting Accuracy." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN), 1391–98. IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847408.

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Li, Qiyang, Yuyan Qiu, Weican Liu, and Jingmin Luan. "Improving Load Forecasting Accuracy through Data Augmentation and TCNs-Transformer." In 2024 4th Power System and Green Energy Conference (PSGEC), 65–69. IEEE, 2024. http://dx.doi.org/10.1109/psgec62376.2024.10721021.

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Tang, Jun, Jingpeng Sun, Bing Guo, Yan Shen, Shengxin Dai, and Peng Wang. "Enhancing Sales Forecasting Accuracy in the Presence of Missing Data." In 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics, 105–11. IEEE, 2024. http://dx.doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00039.

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George, Jossy, Jai Yadav, Akhil M. Nair, Peter M. V, Bosco Paul Alapatt, and Riya Baby. "Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach." In 2024 3rd International Conference for Advancement in Technology (ICONAT), 1–7. IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10775242.

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Zhao, Peinan, Jin Yan, Yumeng A, Yichen Yang, Yidong Li, and Hongwei Zhao. "Reconciling the Accuracy-Speed in Traffic Field’s Time Series Forecasting." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), 1887–94. IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00257.

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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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Imas, Alex, Minah Jung, Silvia Saccardo, and Joachim Vosgerau. The Impact of Joint versus Separate Prediction Mode on Forecasting Accuracy. Cambridge, MA: National Bureau of Economic Research, October 2022. http://dx.doi.org/10.3386/w30611.

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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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Fisher, Andmorgan, Taylor Hodgdon, and Michael Lewis. Time-series forecasting methods : a review. Engineer Research and Development Center (U.S.), November 2024. http://dx.doi.org/10.21079/11681/49450.

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Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, three performance measures were used in this study: mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE).
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Del Negro, Marco, Keshav Dogra, Aidan Gleich, Pranay Gundam, Donggyu Lee, Ramya Nallamotu, and Brian Pacula. The New York Fed DSGE Model: A Post-Covid Assessment. Federal Reserve Bank of New York, January 2024. http://dx.doi.org/10.59576/sr.1082.

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We document the real-time forecasting performance for output and inflation of the New York Fed dynamic stochastic general equilibrium (DSGE) model since 2011. We find the DSGE's accuracy to be comparable to that of private forecasters before Covid, but somewhat worse thereafter.
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6

Martínez-Rivera, Wilmer, Eliana R. González-Molano, and Edgar Caicedo-García. Forecasting Inflation from Disaggregated Data: The Colombian case. Banco de la República, October 2023. http://dx.doi.org/10.32468/be.1251.

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Based on monthly disaggregated Consumer Price Index (CPI) item series and macroeconomic series, we explore the advantages of forecast inflation from a disaggregated to an aggregated level by aggregating the forecasts. We compare the performance of this approach with the forecast obtained modeling aggregated inflation directly. For the aggregate level, we implement some of the techniques and models, helpful to work with many predictors, such as dimension reduction, shrinkage methods, and machine learning models. Also, we implement traditional time-series models. For the disaggregated data, we use its lags and a set of macroeconomic variables as explanatory variables. Direct and recursive forecast techniques are also explored. The sample period of the analysis is from 2011 to 2022, with forecasting and evaluation out of the sample from 2017. In addition, we evaluate the forecast accuracy during the COVID-19 period. We found a reduction in the forecast error from the disaggregate analysis over the aggregate one.
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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, June 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the other LSTM application and ARIMA models optimized for forecasting (with and without explanatory variables). This improvement in forecasting accuracy is most pronounced over longer time horizons, specifically from the seventh month onwards.
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Kent, James. Literature Review: How Artificial Intelligence Impacts the Accuracy and Efficiency of Forecasting in the Finance and Banking Sector. Iowa--Ames: Iowa State University, December 2024. https://doi.org/10.31274/cc-20250502-35.

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Fischer, Eric, Rebecca McCaughrin, Saketh Prazad, and Mark Vandergon. Fed Transparency and Policy Expectation Errors: A Text Analysis Approach. Federal Reserve Bank of New York, November 2023. http://dx.doi.org/10.59576/sr.1081.

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This paper seeks to estimate the extent to which market-implied policy expectations could be improved with further information disclosure from the FOMC. Using text analysis methods based on large language models, we show that if FOMC meeting materials with five-year lagged release dates—like meeting transcripts and Tealbooks—were accessible to the public in real time, market policy expectations could substantially improve forecasting accuracy. Most of this improvement occurs during easing cycles. For instance, at the six-month forecasting horizon, the market could have predicted as much as 125 basis points of additional easing during the 2001 and 2008 recessions, equivalent to a 40-50 percent reduction in mean squared error. This potential forecasting improvement appears to be related to incomplete information about the Fed’s reaction function, particularly with respect to financial stability concerns in 2008. In contrast, having enhanced access to meeting materials would not have improved the market’s policy rate forecasting during tightening cycles.
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Semerikov, Serhiy, Hanna Kucherova, Vita Los, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, April 2021. http://dx.doi.org/10.31812//123456789/4364.

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The prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the business climate index were identified, in particular, the annual GDP growth rate and the volume of retail sales. The similarity of the trends in the calculated and actual business climate index was obtained, the forecast values were calculated with an accuracy of 89.38%. And also, the obtained modeling results were developed by means of building and using neural networks with learning capabilities, which makes it possible to improve the quality and accuracy of the business climate index forecast up to 96.22%. It has been established that the consequences of the impact of COVID-19 are forecasting a decrease in the level of the country's business climate index in the 3rd quarter of 2020. The proposed approach to modeling the country's business climate is unified, easily applied to the macroeconomic data of various countries, demonstrates a high level of accuracy and quality of forecasting. The prospects for further research are modeling the business climate of the countries of the world in order to compare trends and levels, as well as their changes under the influence of quarantine restrictions.
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