Financial Time Series Forecast Using Neural Network Ensembles

Abstract. Financial time series has been standard complex problem in the field of forecasting due to its non-linearity and high volatility. Though various neural networks such as backpropagation, radial basis, recurrent and evolutionary etc. can be used for time series forecasting, each of them suffer from some flaws. Performances are more varied for different time series with loss of generalization. Each of the method poses some pros and cons for it. In this paper, we use ensembles of neural networks to get better performance for the financial time series forecasting. For neural network ensemble four different modules has been used and results of them are finally integrated using integrator to get the final output. Gating has been used as integration techniques for the ensembles modules. Empirical results obtained from ensemble approach confirm the outperformance of forecast results than single module results

An optimized wavelet neural network model for stock market prediction integrated with genetic algorithm

Stock market is a highly volatile domain. Actually, it has always been a challenge to researchers over the world. In recent years, intelligent methodology like wavelet neural network has been employed in this field. However, wavelet neural network based on the theory of the backpropagation (BP) algorithm has two prominent vulnerabilities: low convergence rate and the easily converging to local minimum point. Thus this project establishes an optimized stock price prediction model based on genetic algorithm and wavelet neural network. This model integrates time-frequency localization of the wavelet neural networks and the global optimization searching performance of genetic algorithm. The results prove that this model can substantially improve the forecasting precision performance than other traditional artificial neural network and wavelet neural network. Furthermore it can also avoid the intrinsic defects of the BP algorithm. To validate prediction performance, the result has been compared with 3 research papers, “Stocks Market Modeling and Forecasting Based on HGA and wavelet neural networks” written by Zhou and Wei [11] as well as “Wavelet Transform, Neural Networks and The Prediction of S&P Price Index: A Comparative Study of backpropagation Numerical Algorithms” [32] and “A Comparative Study of backpropagation Algorithms in Financial Prediction”[31] written By Lahmiri. Results show that, our optimized system demonstrates outstanding performance over other systems and achieve very good prediction accuracy in terms of both value and direction.Bachelor of Engineerin