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Original Articles

Modelling intermittent time series and forecasting COVID-19 spread in the USA

Pages 465-475 | Received 19 Jul 2020, Accepted 11 Mar 2022, Published online: 04 Apr 2022

References

  • Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289–303. https://doi.org/10.1057/jors.1972.50
  • Harvey, A. C. (1989). Forecasting, structural time series analysis, and the Kalman filter. Cambridge University Press.
  • Hasni, M., Aguir, M., Babai, M., & Jemai, Z. (2019a). On the performance of adjusted bootstrapping methods for intermittent demand forecasting. International Journal of Production Economics, 216, 145–153. https://doi.org/10.1016/j.ijpe.2019.04.005
  • Hasni, M., Aguir, M., Babai, M., & Jemai, Z. (2019b). Spare parts demand forecasting: A review on bootstrapping methods. International Journal of Production Research, 57(15–16), 4791–4804. https://doi.org/10.1080/00207543.2018.1424375
  • Hasni, M., Babai, M., Aguir, M., & Jemai, Z. (2019). An investigation on bootstrapping forecasting methods for intermittent demands. International Journal of Production Economics, 209, 20–29. https://doi.org/10.1016/j.ijpe.2018.03.001
  • Hyndman, R., Akram, M., & Archibald, B. (2008).The admissible parameter space for exponential smoothing models. Annals of the Institute of Statistical Mathematics, 60(2), 407–426. https://doi.org/10.1007/s10463-006-0109-x
  • Hyndman, R., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for r. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
  • Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Springer Science & Business Media.
  • Kourentzes, N., & Petropoulos, F. (2014). tsintermittent: Intermittent time series forecasting. R package version 1.5. http://CRAN.R-project.org/
  • Mathematica, W. (2012). version 9.0. 1.0. Wolfram Research Inc.
  • Morlidge, S. (2015). Measuring the quality of intermittent-demand forecasts: It’s worse than we’ve thought! Foresight: The International Journal of Applied Forecasting, 37, 37–42.
  • Nikolopoulos, K. (2021). We need to talk about intermittent demand forecasting. European Journal of Operational Research, 291(2), 549–559. https://doi.org/10.1016/j.ejor.2019.12.046
  • Pennings, C. L., van Dalen, J., & van der Laan, E. A. (2017). Exploiting elapsed time for managing intermittent demand for spare parts. European Journal of Operational Research, 258(3), 958–969. https://doi.org/10.1016/j.ejor.2016.09.017
  • Sbrana, G., & Silvestrini, A. (2014). Random switching exponential smoothing and inventory forecasting. International Journal of Production Economics, 156, 283–294. https://doi.org/10.1016/j.ijpe.2014.06.016
  • Sbrana, G., & Silvestrini, A. (2019). Random switching exponential smoothing: A new estimation approach. International Journal of Production Economics, 211, 211–220. https://doi.org/10.1016/j.ijpe.2019.01.038
  • Shale, E. A., Boylan, J. E., & Johnston, F. (2006). Forecasting for intermittent demand: The estimation of an unbiased average. Journal of the Operational Research Society, 57(5), 588–592. https://doi.org/10.1057/palgrave.jors.2602031
  • Shenstone, L., & Hyndman, R. J. (2005). Stochastic models underlying Croston’s method for intermittent demand forecasting. Journal of Forecasting, 24(6), 389–402. https://doi.org/10.1002/for.963
  • Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. International Journal of Production Economics, 71(1–3), 457–466. https://doi.org/10.1016/S0925-5273(00)00143-2
  • Syntetos, A. A., & Boylan, J. E. (2005). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303–314. https://doi.org/10.1016/j.ijforecast.2004.10.001
  • Teunter, R. H., Syntetos, A. A., & Babai, M. Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606–615. https://doi.org/10.1016/j.ejor.2011.05.018
  • Viswanathan, S., & Zhou, C. (2008). A new bootstrapping based method for forecasting and safety stock determination for intermittent demand items. In Nanyang Business School, Nanyang Technological University Singapore Working paper.
  • Willemain, T. R., Smart, C. N., & Schwarz, H. F. (2004). A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting, 20(3), 375–387. https://doi.org/10.1016/S0169-2070(03)00013-X
  • Zhou, C., & Viswanathan, S. (2011). Comparison of a new bootstrapping method with parametric approaches for safety stock determination in service parts inventory systems. International Journal of Production Economics, 133(1), 481–485. https://doi.org/10.1016/j.ijpe.2010.09.021

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