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25 Years of IIF Time Series Forecasting: A Selective Review

Author

Listed:
  • Jan G. De Gooijer
  • Rob J. Hyndman

Abstract

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.

Suggested Citation

  • Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2005-12
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2005/wp12-05.pdf
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    Cited by:

    1. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    2. Prestwich, S.D. & Tarim, S.A. & Rossi, R. & Hnich, B., 2014. "Forecasting intermittent demand by hyperbolic-exponential smoothing," International Journal of Forecasting, Elsevier, vol. 30(4), pages 928-933.
    3. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    4. Filelis - Papadopoulos, Christos K. & Kyziropoulos, Panagiotis E. & Morrison, John P. & O‘Reilly, Philip, 2022. "Modelling and forecasting based on recursive incomplete pseudoinverse matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 358-376.
    5. Ahmad Alsharef & Sonia & Karan Kumar & Celestine Iwendi, 2022. "Time Series Data Modeling Using Advanced Machine Learning and AutoML," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    6. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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