References
- Archibald, B. C. 1990. Parameter space of the Holt-Winters’ model. International Journal of Forecasting 6 (2):199–209. doi:https://doi.org/10.1016/0169-2070(90)90005-V.
- Arora, S., and J. W. Taylor. 2013. Short-term forecasting of anomalous load using rule-based triple seasonal methods. IEEE Transactions on Power Systems 28 (3):3235–42. doi:https://doi.org/10.1109/TPWRS.2013.2252929.
- Bermúdez, J. D., A. Corberán-Vallet, and E. Vercher. 2009. Multivariate exponential smoothing: A Bayesian forecast approach based on simulation. Mathematics and Computers in Simulation 79 (5):1761–69. doi:https://doi.org/10.1016/j.matcom.2008.09.004.
- Bermúdez, J. D., J. V. Segura, and E. Vercher. 2010. Bayesian forecasting with the Holt–Winters model. Journal of the Operational Research Society 61 (1):164–71. doi:https://doi.org/10.1057/jors.2008.152.
- Burkom, H. S., S. P. Murphy, and G. Shmueli. 2007. Automated time series forecasting for biosurveillance. Statistics in mMedicine 26 (22):4202–18. doi:https://doi.org/10.1002/sim.2835.
- Camelo, H. d N., P. S. Lucio, J. B. V. Leal Junior, P. C. M. d Carvalho, and D. v G. d Santos. 2018. Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks. Energy 151:347–57. doi:https://doi.org/10.1016/j.energy.2018.03.077.
- Chatfield, C., and M. Yar. 1988. Holt-Winters forecasting: Some practical issues. Journal of the Royal Statistical Society: Series D (The Statistician) 37 (2):129–40. doi:https://doi.org/10.2307/2348687.
- Chen, Y., W. Lifeng, L. Lianyi, and Z. Kai. 2020. Fractional Hausdorff grey model and its properties. Chaos, Solitons & Fractals 138:109915. doi:https://doi.org/10.1016/j.chaos.2020.109915.
- Colin Cameron, A., and F. A. G. Windmeijer. 1997. An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics 77 (2):329–42. doi:https://doi.org/10.1016/S0304-4076(96)01818-0.
- Croux, C., S. Gelper, and R. Fried. 2008. Computational aspects of robust Holt-Winters smoothing based on M-estimation. Applications of Mathematics 53 (3):163–76. doi:https://doi.org/10.1007/s10492-008-0002-4.
- Dantas, T. M., F. L. Cyrino Oliveira, and H. M. Varela Repolho. 2017. Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management 59:116–23. doi:https://doi.org/10.1016/j.jairtraman.2016.12.006.
- Elbert, Y., and H. S. Burkom. 2009. Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data. Statistics in Medicine 28 (26):3226–48. doi:https://doi.org/10.1002/sim.3708.
- Ferbar Tratar, L., and E. Strmčnik. 2016. The comparison of Holt–Winters method and Multiple regression method: A case study. Energy 109:266–76. doi:https://doi.org/10.1016/j.energy.2016.04.115.
- Ferbar Tratar, L., B. Mojškerc, and A. Toman. 2016. Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics 181:162–73. doi:https://doi.org/10.1016/j.ijpe.2016.08.004.
- Grubb, H., and A. Mason. 2001. Long lead-time forecasting of UK air passengers by Holt–Winters methods with damped trend. International Journal of Forecasting 17 (1):71–82. doi:https://doi.org/10.1016/S0169-2070(00)00053-4.
- Jiang, W., X. Wu, Y. Gong, W. Yu, and X. Zhong. 2020. Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption. Energy 193:116779. doi:https://doi.org/10.1016/j.energy.2019.116779.
- Lawton, R. 1998. How should additive Holt–Winters estimates be corrected? International Journal of Forecasting 14 (3):393–403. doi:https://doi.org/10.1016/S0169-2070(98)00040-5.
- Liu, F., W. Guo, R. Liu, and J. Liu. 2019. Improved load forecasting model based on two-stage optimization of gray model with fractional order accumulation and Markov chain. Communications in Statistics—Theory and Methods. Advance online publication. doi:https://doi.org/10.1080/03610926.2019.1674873.
- Liu, S., and Y. Lin. 2010. Grey systems: Theory and applications. London: Springer.
- Maia, A. L. S., and F. d A. T. de Carvalho. 2011. Holt’s exponential smoothing and neural network models for forecasting interval-valued time series. International Journal of Forecasting 27 (3):740–59. doi:https://doi.org/10.1016/j.ijforecast.2010.02.012.
- Makridakis, S., E. Spiliotis, and V. Assimakopoulos. 2020. The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1):54–74. doi:https://doi.org/10.1016/j.ijforecast.2019.04.014.
- Matsumoto, M., and S. Komatsu. 2015. Demand forecasting for production planning in remanufacturing. The International Journal of Advanced Manufacturing Technology 79 (1–4):161–75. doi:https://doi.org/10.1007/s00170-015-6787-x.
- Puah, Y. J., Y. F. Huang, K. C. Chua, and T. S. Lee. 2016. River catchment rainfall series analysis using additive Holt–Winters method. Journal of Earth System Science 125 (2):269–83. doi:https://doi.org/10.1007/s12040-016-0661-6.
- Segura, J. V., and E. Vercher. 2001. A spreadsheet modeling approach to the Holt–Winters optimal forecasting. European Journal of Operational Research 131 (2):375–88. doi:https://doi.org/10.1016/S0377-2217(00)00062-X.
- Szmit, M., and A. Szmit. 2011. Use of Holt–Winters method in the analysis of network traffic: Case study. In Computer networks, ed. A. Kwiecień, P. Gaj, and P. Stera, 224–31. Berlin, Heidelberg: Springer Berlin Heidelberg.
- Taylor, J. W. 2003. Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society 54 (8):799–805. doi:https://doi.org/10.1057/palgrave.jors.2601589.
- Ventura, L. M. B., F. de Oliveira Pinto, L. M. Soares, A. S. Luna, and A. Gioda. 2019. Forecast of daily PM2.5 concentrations applying artificial neural networks and Holt–Winters models. Air Quality, Atmosphere & Health 12 (3):317–25. doi:https://doi.org/10.1007/s11869-018-00660-x.
- Wang, Q., X. Song, and R. Li. 2018. A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production. Energy 165:1320–31. doi:https://doi.org/10.1016/j.energy.2018.10.032.
- Winters, P. R. 1960. Forecasting sales by exponentially weighted moving averages. Management Science 6 (3):324–42. doi:https://doi.org/10.1287/mnsc.6.3.324.
- Wu, L., S. Liu, and Y. Yang. 2016. Grey double exponential smoothing model and its application on pig price forecasting in China. Applied Soft Computing 39:117–23. doi:https://doi.org/10.1016/j.asoc.2015.09.054.
- Wu, L., S. Liu, L. Yao, S. Yan, and D. Liu. 2013. Grey system model with the fractional order accumulation. Communications in Nonlinear Science and Numerical Simulation 18 (7):1775–85. doi:https://doi.org/10.1016/j.cnsns.2012.11.017.
- Wu, W., X. Ma, Y. Wang, Y. Zhang, and B. Zeng. 2019. Research on a novel fractional GM(α, n) model and its applications. Grey Systems: Theory and Application 9 (3):356–73. doi:https://doi.org/10.1108/GS-11-2018-0052.
- Zhao, H., X. Han, and S. Guo. 2018. DGM (1, 1) model optimized by MVO (multi-verse optimizer) for annual peak load forecasting. Neural Computing and Applications 30 (6):1811–25. doi:https://doi.org/10.1007/s00521-016-2799-1.