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Time Series Analysis and Data Relationships

Year 2015, Volume: 3 , 225 - 230, 30.12.2015

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.

References

  • [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.
Year 2015, Volume: 3 , 225 - 230, 30.12.2015

Abstract

References

  • [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.
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Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Articlessi
Authors

Yunus Bıcen

Mustafa Kayıkcı This is me

Faruk Aras

Publication Date December 30, 2015
Published in Issue Year 2015 Volume: 3

Cite

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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