Computational Intelligence in Time Series Forecasting: by Ajoy K. Palit

By Ajoy K. Palit

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Extra info for Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control)

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In this case I( f ) is referred to as the sample spectrum. , X n )T T (a0 , a, b) and ª1 cos Z sin Z º « » «1 cos 2Z sin 2Z » ». A= « «... .... » « » ¬«1 cos nZ sin nZ »¼ Minimizing the least-squares n 2 ¦ ( X t  a0  a cos Z t  b sin Z t ) t 1 the ș can be estimated using the pseudo inverse relation Tˆ ( AT A) 1 AT X . In order to use the spectral expansion technique for forecasting purposes, we need first to observe the given time series carefully to check whether it contains any trend and/or seasonality.

Once the series is de-trended, we have to check for various frequency components present in the residual of the time series. This is accomplished by first transforming the signals from the time domain into the frequency domain using a fast Fourier transform 42 Computational Intelligence in Time Series Forecasting (FFT), and then by computing the power spectral density function as a measure of the energy at various frequencies Pyy Y * Y where “*” represents the one-to-one multiplication of vector components or of matrix elements, and Y represents the complex conjugate of Y.

49] Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioural science. PhD Thesis, Harvard University, Cambridge, MA. [50] Zadeh LA (1965)Fuzzy sets. Information and Control, 8: 338-353. [51] Zadeh LA (1979) A theory of approximate reasoning. In: Hayes P, Michie D, and Mikulich I, eds. : Machine Intelligence, Halstead Press, New York: 149-194. [52] Zadeh LA (1993) Fuzzy logic, neural networks, and soft computing. Proc. IEEE Int. Workshop Neuro Fuzzy Control, Muroran, Japan: 1-3.

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