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Dergiler / Balkan Journal of Electrical and Computer Engineering / 2015 / Cilt: 3

Time Series Analysis and Data Relationships

Yunus BICEN
Mustafa KAYIKCI
Faruk ARAS
Sayfa
225–230
Yayın Tarihi
2015-12-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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.
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Temel Bulgular & Veri:Produced results of these methods which are diversified in time, is variable for different data sets.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Exponential smoothing · forecasting methods · time series

Abstract

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.

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Bu Makaleye Atıf Yap

BICEN, Y., KAYIKCI, M., ARAS, F. (2015). Time Series Analysis and Data Relationships. Balkan Journal of Electrical and Computer Engineering, Cilt: 3, 225–230. https://ajindex.com/articles/473268

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Kaynakça (17)

  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.