شماره ركورد كنفرانس :
3385
عنوان مقاله :
Prediction of Stock Index Behavior using a Hybrid Model of Artificial Neural Network and Wavelet Transform
پديدآورندگان :
Karimi Dastjerdi M.H Industrial Engineering and Management Systems Amirkabir University of Technology Tehran , Mirjalili Mahdi Industrial Engineering and Management Systems Amirkabir University of Technology Tehran , Mehran Pejman Industrial Engineering and Management Systems Amirkabir University of Technology Tehran
كليدواژه :
Stock index forecasting , Hybrid model , Wavelet transform , NARX neural networks
عنوان كنفرانس :
دومين كنگره بين المللي مهندسي صنايع و سيستم ها
چكيده لاتين :
Predicting the future values of time series (signals)
based on the past and present data can be a useful tool for financial
applications. In this study, the stock index prediction is made using
a hybrid model, composed of the wavelet transform and Nonlinear
Auto-Regressive with eXogenous Inputs (NARX) neural network.
This model comprises two stages. First, the preprocessing stage in
which the Haar wavelet transform and a soft thresholding
approach are used to de-noise the macroeconomic variables (oil
prices and Rial to Dollar exchange rate). Second, the NARX neural
network is developed to use the de-noised external inputs to
predict the stock index as the output. By comparing the results of
NARX neural network and the hybrid model, it is shown that the
proposed model is more accurate than NARX neural network, and
can achieve more reliable results.
