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Financial time series prediction is considered as a challenging task. The task becomes difficult due to inherent nonlinear and non-stationary characteristics of financial time series. This article proposes a combination of wavelet and Postfix-GP, a postfix notation based genetic programming system, for financial time series prediction. The discrete wavelet transform approach is used to smoothen the...
Traditional techniques for time series modeling can capture linear behavior of data and lack the ability to identify nonlinear patterns in time series. Therefore, machine learning techniques like Neural Network or Genetic Programming (GP) are used by practitioners for modeling nonlinear and irregular time series. GP is preferred over other techniques because it does not presume model structure a priori...
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