Araştırma Makalesi
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Time Series Analysis and Data Relationships

Yıl 2015, Cilt: 3 , 225 - 230, 30.12.2015

Öz

The time-series models which has an has an important place in the statistical forecasting methods are widely used in many disciplines such as economy, production management, and engineering in order to perform realistic estimates for the future. Produced results of these methods which are diversified in time, is variable for different data sets. A model that produces pretty good results for a dataset may not be realistic for the other dataset. The success of the time-series forecasting methods is directly related to the quantitative characteristic features of a dataset ranked through time. In this study, it is tried to identify the main principles for determining the correct method and suitably selecting the parameters within the framework of time-series forecasting models and quantitative characteristics of the data sets.

Kaynakça

  • [1] E.S. Gardner, “Exponential smoothing: the state of the art”, Journal of Forecasting vol. 4, no. 1, pp. 1–28, 1985. [2] E.S. Gardner, “Exponential smoothing: the state of the art—part II”, International Journal of Forecasting, vol. 22, pp. 637–666, 2006. [3] J.T. Mentzer, M.A. Moon, “Sales Forecasting Management: A Demand Management Approach”, Sage Puplications, UK, 2005. [4] J.S. William, “Operations Management”, McGraw-Hill Higher Education,USA, 2012. [5] J.W. Taylor, Smooth Transition Exponential Smoothing, Journal of Forecasting, vol. 23, pp. 385–404, 2004. [6] J.T. Mentzer, “Forecasting with adaptive extended exponential smoothing”, Journal of the Academy of Marketing Science, vol. 16, no. 3-4, pp. 62-70, 1988. [7] R.J. Hyndman, A.B. Koehler, R.D. Snyder, G. Simone , “A state space framework for automatic forecasting using exponential smoothing methods”, International Journal of Forecasting, vol. 18, no.3, pp. 439–454, 2002. [8] C.C. Pegels, “Exponential forecasting: some new variations”, Management Science, vol. 12 pp. 311–315, 1969. [9] D.W. Trigg, A.G. Leach, “Exponential smoothing with an adaptive response rate”, Operational Research Quarterly, vol. 18, no. 1, pp. 53–59, 1967. [10] S. Ekern, “Adaptive exponential smoothing revisited”, The Journal of the Operational Research Society, vol. 32, no. 9 pp. 775-782, 1981. [11] S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler, “The accuracy of extrapolation (time series) methods: Results of a forecasting competition”, Journal of Forecasting, vol. 1, pp. 111-153, 1982. [12] D.C. Whybark, “Comparison of adaptive forecasting techniques”, Logistics Transportation Review, vol. 8, pp. 13–26, 1973. [13] D.W. Bunn, “Adaptive forecasting using the Kalman filter”, Omega, vol. 9, pp. 323–324, 1981. [14] P.C. Young, “Nonstationary time series analysis and forecasting”, Progress in Environmental Science, vol. 1, pp. 3–48, 1999. [15] J.T. Mentzer, R. Gomes, “Further extensions of adaptive extended exponential smoothing and comparison with the M-Competition”, Journal of the Academy of Marketing Science, vol. 22, pp. 372–382, 1994. [16] S.N. Pantazopoulos, C.P. Pappis, “A new adaptive method for extrapolative forecasting algorithms”, European Journal of Operational Research, vol. 94, pp. 106–111, 1996. [17] C.W.J. Granger, T. Teräsvirta, “Modelling nonlinear economic relationships, advanced texts in econometrics”, Oxford University Press: New York, 1993.
Yıl 2015, Cilt: 3 , 225 - 230, 30.12.2015

Öz

Kaynakça

  • [1] E.S. Gardner, “Exponential smoothing: the state of the art”, Journal of Forecasting vol. 4, no. 1, pp. 1–28, 1985. [2] E.S. Gardner, “Exponential smoothing: the state of the art—part II”, International Journal of Forecasting, vol. 22, pp. 637–666, 2006. [3] J.T. Mentzer, M.A. Moon, “Sales Forecasting Management: A Demand Management Approach”, Sage Puplications, UK, 2005. [4] J.S. William, “Operations Management”, McGraw-Hill Higher Education,USA, 2012. [5] J.W. Taylor, Smooth Transition Exponential Smoothing, Journal of Forecasting, vol. 23, pp. 385–404, 2004. [6] J.T. Mentzer, “Forecasting with adaptive extended exponential smoothing”, Journal of the Academy of Marketing Science, vol. 16, no. 3-4, pp. 62-70, 1988. [7] R.J. Hyndman, A.B. Koehler, R.D. Snyder, G. Simone , “A state space framework for automatic forecasting using exponential smoothing methods”, International Journal of Forecasting, vol. 18, no.3, pp. 439–454, 2002. [8] C.C. Pegels, “Exponential forecasting: some new variations”, Management Science, vol. 12 pp. 311–315, 1969. [9] D.W. Trigg, A.G. Leach, “Exponential smoothing with an adaptive response rate”, Operational Research Quarterly, vol. 18, no. 1, pp. 53–59, 1967. [10] S. Ekern, “Adaptive exponential smoothing revisited”, The Journal of the Operational Research Society, vol. 32, no. 9 pp. 775-782, 1981. [11] S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler, “The accuracy of extrapolation (time series) methods: Results of a forecasting competition”, Journal of Forecasting, vol. 1, pp. 111-153, 1982. [12] D.C. Whybark, “Comparison of adaptive forecasting techniques”, Logistics Transportation Review, vol. 8, pp. 13–26, 1973. [13] D.W. Bunn, “Adaptive forecasting using the Kalman filter”, Omega, vol. 9, pp. 323–324, 1981. [14] P.C. Young, “Nonstationary time series analysis and forecasting”, Progress in Environmental Science, vol. 1, pp. 3–48, 1999. [15] J.T. Mentzer, R. Gomes, “Further extensions of adaptive extended exponential smoothing and comparison with the M-Competition”, Journal of the Academy of Marketing Science, vol. 22, pp. 372–382, 1994. [16] S.N. Pantazopoulos, C.P. Pappis, “A new adaptive method for extrapolative forecasting algorithms”, European Journal of Operational Research, vol. 94, pp. 106–111, 1996. [17] C.W.J. Granger, T. Teräsvirta, “Modelling nonlinear economic relationships, advanced texts in econometrics”, Oxford University Press: New York, 1993.
Toplam 1 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Yunus Bıcen

Mustafa Kayıkcı Bu kişi benim

Faruk Aras

Yayımlanma Tarihi 30 Aralık 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 3

Kaynak Göster

APA Bıcen, Y., Kayıkcı, M., & Aras, F. (2015). Time Series Analysis and Data Relationships. Balkan Journal of Electrical and Computer Engineering, 3, 225-230.

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