The Decomposition Issue of a Time Series in the Forecasting Process

[1] Akaike H. A New Look at the Statistical Model Identification. I.E.E.E. Transactions on Automatic Control. No. 19, 1974.10.1109/TAC.1974.1100705Search in Google Scholar

[2] Barbosa R. M., Silva M. E., Fernandes M. J. Time Series Analysis of Sea-Level Records: Characterising Long-Term Variability, edited by Donner R. V., Barbosa R. M. Nonlinear Time Series Analysis in the Geosciences: Applications in Climatology, Geodynamics and Solar-Terrestrial Physics. Springer-Verlag Berlin Heidelberg, 2008, pp. 158. Search in Google Scholar

[3] Chatfield C. Time-series Forecasting. CRC Press. 2001, pp. 21. 10.1201/9781420036206Search in Google Scholar

[4] Dickey D.A., Fuller W.A. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, No. 74, 1979.10.2307/2286348Search in Google Scholar

[5] Hannan E.J., Quinn B.G. The Determination of the Order of an Autoregression. Journal of the Royal Statistical Society, B. 1979, No. 41.10.1111/j.2517-6161.1979.tb01072.xSearch in Google Scholar

[6] Katznelson Y. An introduction to Harmonic Analysis, 3rd Edition. Cambridge University Press, 2004.10.1017/CBO9781139165372Search in Google Scholar

[7] Khandelwal I., Adhikari R., Verma G. International Conference On Computer, Communication And Convergence (ICCC 2015). Procedia Computer Science. Vol. 48, 2015, pp. 173-179.10.1016/j.procs.2015.04.167Search in Google Scholar

[8] de Oliveira J., Ludermir T. A hybrid evolutionary decomposition system for time series forecasting. Neurocomputing. Vol. 180, 2015, pp. 27-34.10.1016/j.neucom.2015.07.113Search in Google Scholar

[9] Qiu X., Ren Y., Suganthan P. N., Amaratunga G. A. J. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting. Applied Soft Computing, Vol. 54, 2017, pp. 246-255.10.1016/j.asoc.2017.01.015Search in Google Scholar

[10] Schwarz G. Estimating the Dimension of a Model. Annals of Statistics, 1978, No. 6.10.1214/aos/1176344136Search in Google Scholar

[11] Wei L. Y. A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing. Vol. 42, 2016, pp. 368-376.10.1016/j.asoc.2016.01.027Search in Google Scholar

[12] Yaffee R., McGee M. Introduction to Time Series Analysis and Forecasting with Applications of SAS and SPSS, Academic Press, 2000, pp. 476.Search in Google Scholar