Forecasting Indian Stock Market Index Using Singular Spectrum Analysis: A Comparison with ARIMA and Artificial Neural Network

2017 ◽  
Author(s):  
Suwarna Shukla ◽  
Krishna Prasad Yadav
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Stock Market Index ◽  
Singular Spectrum ◽  
Market Index ◽  
Artificial Neural ◽  
Indian Stock Market

scholarly journals A hybrid model coupled with singular spectrum analysis for daily rainfall prediction

2010 ◽  
Vol 12 (4) ◽  
pp. 458-473 ◽  
Author(s):  
K. W. Chau ◽  
C. L. Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Daily Rainfall ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Singular Spectrum ◽  
Artificial Neural

A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered.

scholarly journals Stock market index prediction using artificial neural network

2016 ◽  
Vol 21 (41) ◽  
pp. 89-93 ◽  
Author(s):  
Amin Hedayati Moghaddam ◽  
Moein Hedayati Moghaddam ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Stock Market Index ◽  
Market Index ◽  
Artificial Neural
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