ATA Method

Year 2019, Volume: 48 Issue: 6, 1838 - 1844, 08.12.2019

Guckan Yapar , Hanife Taylan Selamlar Sedat Capar , İdil Yavuz

https://doi.org/10.15672/hujms.461032

Cited By: 4

Abstract

In this study, the forecasting accuracy of a new forecasting method that is alternative to two major forecasting approaches: exponential smoothing (ES) and ARIMA, will be evaluated. Using the results from the M3-competition, the forecasting performance of this method will be compared to not only these two major approaches but also to other successful methods derived from these two approaches with respect to simplicity and cost in addition to accuracy.

Keywords

Exponential smoothing, Forecasting, Initial value, M3-competition, Smoothing parameter

References

• [1] S. Makridakis and M. Hibon, The M3-Competition: results, conclusions and implications, Int. J. Forecast, 16 (4), 451–476, 2000.
• [2] J. De Gooijer and R. Hyndman, 25 years of IIF time series forecasting: A selective review, Int. J. Forecast, 22 (3), 443–473, 2006.
• [3] P. Goodwin, The holt-winters approach to exponential smoothing: 50 years old and going strong, Foresight, 19, 30–33, 2010.
• [4] R. Hyndman, A. Koehler, R. Snyder and S. Grose, A state space framework for automatic forecasting using exponential smoothing methods, Int. J. Forecast, 13 (3), 439–454, 2002.
• [5] R. Hyndman, A. Koehler, J. Ord and R. Snyder, Forecasting with exponential smoothing: the state space approach, Springer-Verlag, 2008.
• [6] R. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, OTexts, 2014.
• [7] C. Pegels, On startup or learning curves: An expanded view, AIIE Transactions, 1 (3), 216–222, 1969.
• [8] E. Gardner Jr and E. McKenzie, Forecasting trends in time series, Management Science, 31 (10), 1237–1246, 1985.
• [9] S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen and R. Winkler, The forecasting accuracy of major time series methods, Wiley, 1984.
• [10] A. Koning, P. Franses, M. Hibon abd H. Stekler, The M3 competition: Statistical tests of the results, Int. J. Forecast, 21 (3), 397–409, 2005.
• [11] V. Assimakopoulos and K. Nikolopoulos, The theta model: a decomposition approach to forecasting, Int. J. Forecast, 16 (4), 521–430, 2000.
• [12] R. Hyndman and B. Billah, Unmasking the Theta method, Int. J. Forecast, 19 (2), 287–290, 2003.
• [13] J. Bates and C. Granger, The combination of forecasts, Journal of the Operational Research Society, 20 (4), 451–468, 1969.
• [14] R. Clemen, Combining forecasts: A review and annotated bibliography, Int. J. Forecast, 5 (4), 559–583, 1989.
• [15] J. Armstrong, Combining forecasts: The end of the beginning or the beginning of the end?, Int. J. Forecast, 5 (4), 585–588, 1989.
• [16] J. Armstrong, Principles of forecasting: a handbook for researchers and practitioners, Springer Science & Business Media, 2001.
• [17] S. Makridakis and R. Winkler, Averages of forecasts: Some empirical results, Management Science, 29 (9), 987–996, 1983.
• [18] S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen and R. Winkler, The accuracy of extrapolation (time series) methods: Results of a forecasting competition, J. Forecast., 1 (2), 111–153, 1982.
• [19] R. Cleveland, W. Cleveland, J. McRae and I. Terpenning, STL: A seasonal-trend decomposition procedure based on loess, Journal of Official Statistics, 6 (1), 3–73, 1990.
• [20] M. Adya, J. Armstrong, F. Collopy and M. Kennedy, An application of rule-based forecasting to a situation lacking domain knowledge, Int. J. Forecast, 16 (4), 477–484, 2000.
• [21] C. Bergmeir, R. Hyndman and J. Benitez, Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation, Int. J. Forecast, 32 (2), 303– 312, 2016.
• [22] G. Yapar, Modified simple exponential smoothing, Hacet. J. Math. Stat., 47 (3), 741– 754, 2018.
• [23] G. Yapar, S. Capar, H. Selamlar and I. Yavuz, Modified Holt’s linear trend method, Hacet. J. Math. Stat., 47 (5), 1394–1403, 2018.