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Performance of 109 Machine Learning Algorithms across Five Forecasting Tasks: Employee Behavior Modeling, Online Communication, House Pricing, IT Support and Demand Planning

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  • Anton A. Gerunov

Abstract

This article puts the problem of forecasting in economic and business situations under scrutiny. Starting from the premise that accurate forecasting is now a key capability for analyzing problems of business operations and public policy, we investigate the performance of alternative prediction methods that include both traditional econometric approaches as well as novel algorithms from the field of machine learning. The article tests a total of 109 different regression-type algorithms across five pertinent business domains – employee absenteeism, success of online communication, real estate asset pricing, support ticket processing, and demand forecasting. The results indicate that forecasting algorithms tend to produce a set of widely dispersed outcome, with some methods such as random forecast and neural network implementations being able to consistently generate superior performance. We further argue that forecast accuracy is not necessarily predicated upon computational complexity and thus, an optimization decision between the costs and benefits of using a certain algorithm can feasibly be made.

Suggested Citation

  • Anton A. Gerunov, 2022. "Performance of 109 Machine Learning Algorithms across Five Forecasting Tasks: Employee Behavior Modeling, Online Communication, House Pricing, IT Support and Demand Planning," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 15-43.
  • Handle: RePEc:bas:econst:y:2022:i:2:p:15-43
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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    3. Kunze, Frederik & Wegener, Christoph & Bizer, Kilian & Spiwoks, Markus, 2017. "Forecasting European interest rates in times of financial crisis – What insights do we get from international survey forecasts?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 192-205.
    4. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    5. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
    6. (Jeremy) Chiu, Ching-wai & Harris, Richard D.F. & Stoja, Evarist & Chin, Michael, 2018. "Financial market Volatility, macroeconomic fundamentals and investor Sentiment," Journal of Banking & Finance, Elsevier, vol. 92(C), pages 130-145.
    7. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    8. Wolter Hassink, 2018. "How to reduce workplace absenteeism," IZA World of Labor, Institute of Labor Economics (IZA), pages 447-447, September.
    9. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
    10. P. Christensen & K. Gillingham & W. Nordhaus, 2018. "Uncertainty in forecasts of long-run economic growth," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(21), pages 5409-5414, May.
    11. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    12. Kris Johnson Ferreira & Bin Hong Alex Lee & David Simchi-Levi, 2016. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 69-88, February.
    13. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
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    More about this item

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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