Wavelet Neural Networks for Nonlinear Time Series Analysis

A wavelet network is an important tool for analyzing time series especially when it is nonlinear and non-stationary. It takes advantage of high resolution of wavelets and learning and feed forward nature of Neural Networks. Wavelets are a class of functions such that multiple resolution nature of wavelets provides a natural frame work for the analysis of time series. The power of this network to approximate functions from given input-output data is proved and it has utilized the localization property of a wavelet to focus on local properties. Guaranteed upper bounds on the accuracy of approximation is established. Here we are analyzing the time series of number of terrorist attacks in the world measured on monthly basis during the period February 1968 to January 2007 for establishing the superiority of this method over other existing methods. The simulation results show that the model is capable of producing a reasonable accuracy within several steps. Mathematics Subject Classication: 37M10, 65T60, 92B20

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