Modified simple exponential smoothing

Year 2018, Volume: 47 Issue: 3, 741 - 754, 01.06.2018

Guckan Yapar

Abstract

In this study, we propose a new exponential smoothing method, modified simple exponential smoothing (MSES) as an alternative to simple exponential smoothing (SES). Despite its success and widespread use in many areas, SES has some shortcomings that negatively affect the
accuracy of forecasts made using this method. For example, there is no agreed upon concensus on choosing an initial value and determining an
optimum smoothing parameter and these decisions greatly affect the forecasting accuracy of SES. The proposed method will help cope with these shortcomings. It is compared to SES on popular metrics that are commonly used for evaluating performance of forecasting techniques and is shown to have better performance. The two models are applied to the 1001 time series of the M-competition data simultaneously and their prediction accuracies are compared under various settings.

Keywords

Forecasting, Initial value, M-competition, Time series

References

• Bailey, W. N., 1935. Generalized hypergeometric series. University Press Cambridge.
• Bowerman, B. L., O'Connell, R. T., 1979. Time series and forecasting. Duxbury Press.
• Brown, R. G., 1959. Statistical forecasting for inventory control. McGraw/Hill.
• Brown, R. G., 1962. Smoothing, forecasting and prediction of discrete time series. Prentice-Hall.
• Gardner, E. S., 1985. Exponential smoothing: The state of the art. Journal of forecasting 4 (1), 128.
• Gardner, E. S., 2006. Exponential smoothing: The state of the artpart ii. International journal of forecasting 22 (4), 637666.
• Gardner Jr, E. S., McKenzie, E., 1985. Forecasting trends in time series. Management Science 31 (10), 12371246.
• Goodwin, P., et al., 2010. The holt-winters approach to exponential smoothing: 50 years old and going strong. Foresight 19, 3033.
• Holt, C., 1957. Forecasting trends and seasonal by exponentially weighted averages. In- ternational Journal of Forecasting 20 (1), 510.
• Hyndman, R. J., Athanasopoulos, G., 2014. Forecasting: principles and practice. OTexts.
• Hyndman, R. J., Koehler, A. B., Snyder, R. D., Grose, S., 2002. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting 18 (3), 439454.
• Johnson, N. L., Kotz, S., 1977. Urn models and their application: an approach to modern discrete probability theory. Vol. 77. Wiley New York.
• Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R.,
• Newton, J., Parzen, E., Winkler, R., 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of forecasting 1 (2), 111153.
• Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International journal of forecasting 16 (4), 451476.
• Makridakis, S. G., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R.,
• Newton, J., Parzen, E., Winkler, R., 1984. The forecasting accuracy of major time series methods. Wiley.
• Makridakis, S. G., Wheelwright, S. C., 1978. Forecasting: Methods and Applications. Wiley.
• Montgomery, D. C., Johnson, L. A., 1976. Forecasting and time series analysis. McGraw- Hill.
• Pegels, C. C., 1969. On startup or learning curves: An expanded view. AIIE Transactions 1 (3), 216222.
• Taylor, J. W., 2003. Exponential smoothing with a damped multiplicative trend. International journal of Forecasting 19 (4), 715725.
• Winters, P. R., 1960. Forecasting sales by exponentially weighted moving averages. Management Science 6 (3), 324342.
• Yager, R. R., 2008. Time series smoothing and owa aggregation. IEEE Transactions on Fuzzy Systems 16 (4), 9941007.