References
- Box, G. E. P. and Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society B, 26, 211-252.
- Brown, R. G. (1959). Statistical forecasting for inventory control, McGraw-Hill, New York.
- Brown, R. G. (1962). Smoothing, forecasting and prediction of discrete Time Series, Prentice-Hall, New Jersey.
- De Livera, A. M., Hyndman, R. J. and Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106, 1513-1527. https://doi.org/10.1198/jasa.2011.tm09771
- Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted moving averages, Office of Naval Research memorandum, No.52, Carnegie Institute of Technology, Pittsburgh.
- Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moveing averages. International Journal of Forecasting, 20, 5-10 https://doi.org/10.1016/j.ijforecast.2003.09.015
- Kim, C. H. (2013a). Electricity demand patterns analysis by daily and timely time series, Korea Development Institute, Korea Energy Economics Institute, Uiwang.
- Kim, C. H. (2013b). Short-term electricity demand forecasting using complex seasonal exponential smoothing, Korea Development Institute, Korea Energy Economics Institute, Uiwang.
- Lee, Y. S., Kim, J., Jang, M. S. and Kim, H. G. (2013). A study on comparing short-term wind power prediction models in Gunsan wind farm: A case study. Journal of the Korean Data & Information Science Society, 24, 585-592. https://doi.org/10.7465/jkdi.2013.24.3.585
- Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54, 799-805 https://doi.org/10.1057/palgrave.jors.2601589
- Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204, 139-152. https://doi.org/10.1016/j.ejor.2009.10.003
- Winters, P. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324-342. https://doi.org/10.1287/mnsc.6.3.324
- Yoon, S. H. and Choi, Y. J. (2015). Functional clustering for electricity demand data: A case study. Journal of the Korean Data & Information Science Society, 26, 885-894. https://doi.org/10.7465/jkdi.2015.26.4.885
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