An intelligent stock forecasting system using a unify model of CEFLANN, HMM and GA for stock time series phenomena

Abstract

The aim of this work to suggest and apply a unify model by combining the Computationally Efficient Functional Link Artificial Neural Networks (CEFLANN), Hidden Markov Model (HMM), and Genetic Algorithms (GA) to predict future trends from a highly uncertainty stock time series phenomena. We present a framework of an intelligent stock forecasting system using complete features to predict stock trading estimations that may consequence in better profits. Using CEFLANN architecture, the stock prices are altered to independent sets of values that become input to HMM. The trained and tested HMM output is used to identify the trends in the stock time series data. We apply different methods to generate complete features that raise trading decisions from stock price indices. We have used population based optimization tool genetic algorithms (GAs) to optimize the initial parameters of CEFLANN and HMM. Finally, the results achieved from the unified model are compared with CEFLANN and other conventional forecasting methods using performance assessment techniques.