Research Article
COMPARISON OF SINGLE AND MODIFIED EXPONENTIAL SMOOTHING METHODS IN THE PRESENCE OF A STRUCTURAL BREAK
Abstract
Zaman serisi analizlerinde serilerin modellenmesinin,
aralarındaki ilişkilerin incelenmesinin yanında temel amaç geleceğe yönelik
öngörümleme yapmaktır. Literatürde en yaygın kullanılan yöntemlerden biri üssel
düzeltme yöntemleridir. Serilerinin veri üretim süreçlerinde, finansal krizler,
doğal afetler gibi birçok nedenden dolayı kalıcı yapısal değişimler meydana
gelebilmektedir. Bu değişimler model parametrelerini değiştirebildiği gibi
analiz sonuçlarına da etki etmektedirler. Bu çalışmadaki temel amaç, seride
yapısal kırılmalar olduğunda basit üssel düzeltme (SES) ile yeni geliştirilmiş
olan Modifiye Üssel Düzeltme (MSES)(2016) yöntemlerinin tahminleme
performanslarını karşılaştırmaktır. Hata teriminin (MAE) ortalama ve varyansı
örneklemin büyüklüğünden, kırılmanın şiddetinden ve konumundan etkilenmektedir.
Veri setindeki kırılmalar model tahmini olumsuz etkilemektedir. MSES yönteminin
kullanılmasında olası kırılmaların büyüklükleri ve konumları dikkate
alınmalıdır.
References
- Brown, R. G., (1962) Smoothing , Forecasting and Prediction of Discrete Time Series. United State of America: Prentice-Hall.
- Bowerman, B.L., O’Connell, R.T., (1993). Time Series and Forecasting (3 th ed.). Duxburg Press.
- Çapar, S. (2009). Improvement for Exponential Smoothing . Izmir, Turkey: Dokuz Eylül Unıversıty Graduate School of Natural and Applıed Scıences.
- Çoban B. ve Firuzan E.(2016). The Role Of Structural Break And Volatıle Innovatıons On Coıntegratıon Tests: Tsunamı And Global Economıcs Crısıs. International Journal of Arts & Sciences, Cilt: 9, Sayı: 2, ss:211-223
- Dickey, D.A., Fuller, W.A., 1981. Likelihood Ratio Statistics For Autoregressive Time Series With A Unit Root. Econometrica Sayı: 49, ss:1057–1072.
- Giraitis L., Kapetanios G., Mansur. M,. (2015) Forecasting Under Structural Change. Switzerland: Springer
- Hydman, R. J., Koehler, A. B., Ord, J. K., Snyder, R. D., (2008). Forecasting with Exponential Smoothing.Berlin: Springer.
- Makridakis, Spyros, Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E. and Winkler, R. (1982), “The accuracy of extrapolation (time series) methods: Results of a forecasting competition,” Journal of Forecasting, 1, 111-153. (all)
- Montgomaery, D. C., Johnson, L.A., (1976). Forecasting and Time Series Analysis. McGraw-Hill. Nelson, C.R., Plosser, C.I., (1982). Trends And Random Walks İn Macroeconomic Time Series. Journal of Monetary Economics, Sayı:10, ss: 139–162.
- Selamlar, T., H., (2017). Modeling and Forecasting Time Series Data Using Ata Method. İzmir, Turkey: Dokuz Eylül Unıversıty Graduate School of Natural and Applıed Scıences.
- Yapar, G., 2016, “Modified simple exponential smoothing“, Hacettepe Journal of Mathematics and Statistics, Doi:10.15672/HJMS.201614320580
- Yapar, G., Çapar, S., Selamlar, H. T., Yavuz, I., 2016 Modified holt’s linear trend method Hacettepe University Journal of Mathematics and Statistics Submitted, O.
- Zivot, E. ve Andrews, D.W.K. (1992). Further Evidence on great crash, the oil price shock and the unit root hypothesis. Journal of Business and Economic Statistics, Sayı: 10, ss: 251-270
COMPARISON OF SINGLE AND MODIFIED EXPONENTIAL SMOOTHING METHODS IN THE PRESENCE OF A STRUCTURAL BREAK
Abstract
The modeling of the series in the time series analysis, as well as the examination of the relations between them, is the main purpose of the future forecasting. One of the most widely used methods in the literature is exponential smoothing methods. Due to many reasons such as financial crises, natural disasters in the data production processes of the series, permanent structural changes can occur. These changes affect model parameters as well as analysis results. The main purpose of this study is to compare the predictive performances of the newly developed Modified Exponential Smoothing (MSES)(2016) methods with the simple exponential smoothing (SES) when there are structural breaks in the series with different break magnitude and different break location. Mean Absolute Error values of methods are affected by the sample size, break magnitude and location. The breaks in the data set would affect the model estimation negatively. Possible breaks’ magnitude and locations should be taken into consideration in the use of the MSES method.
References
- Brown, R. G., (1962) Smoothing , Forecasting and Prediction of Discrete Time Series. United State of America: Prentice-Hall.
- Bowerman, B.L., O’Connell, R.T., (1993). Time Series and Forecasting (3 th ed.). Duxburg Press.
- Çapar, S. (2009). Improvement for Exponential Smoothing . Izmir, Turkey: Dokuz Eylül Unıversıty Graduate School of Natural and Applıed Scıences.
- Çoban B. ve Firuzan E.(2016). The Role Of Structural Break And Volatıle Innovatıons On Coıntegratıon Tests: Tsunamı And Global Economıcs Crısıs. International Journal of Arts & Sciences, Cilt: 9, Sayı: 2, ss:211-223
- Dickey, D.A., Fuller, W.A., 1981. Likelihood Ratio Statistics For Autoregressive Time Series With A Unit Root. Econometrica Sayı: 49, ss:1057–1072.
- Giraitis L., Kapetanios G., Mansur. M,. (2015) Forecasting Under Structural Change. Switzerland: Springer
- Hydman, R. J., Koehler, A. B., Ord, J. K., Snyder, R. D., (2008). Forecasting with Exponential Smoothing.Berlin: Springer.
- Makridakis, Spyros, Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E. and Winkler, R. (1982), “The accuracy of extrapolation (time series) methods: Results of a forecasting competition,” Journal of Forecasting, 1, 111-153. (all)
- Montgomaery, D. C., Johnson, L.A., (1976). Forecasting and Time Series Analysis. McGraw-Hill. Nelson, C.R., Plosser, C.I., (1982). Trends And Random Walks İn Macroeconomic Time Series. Journal of Monetary Economics, Sayı:10, ss: 139–162.
- Selamlar, T., H., (2017). Modeling and Forecasting Time Series Data Using Ata Method. İzmir, Turkey: Dokuz Eylül Unıversıty Graduate School of Natural and Applıed Scıences.
- Yapar, G., 2016, “Modified simple exponential smoothing“, Hacettepe Journal of Mathematics and Statistics, Doi:10.15672/HJMS.201614320580
- Yapar, G., Çapar, S., Selamlar, H. T., Yavuz, I., 2016 Modified holt’s linear trend method Hacettepe University Journal of Mathematics and Statistics Submitted, O.
- Zivot, E. ve Andrews, D.W.K. (1992). Further Evidence on great crash, the oil price shock and the unit root hypothesis. Journal of Business and Economic Statistics, Sayı: 10, ss: 251-270
There are 13 citations in total.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | January 16, 2018 |
| Published in Issue | Year 2018 18. EYI Special Issue |
Cited By
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https://doi.org/10.15587/1729-4061.2019.162652
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