Access the full text.
Sign up today, get DeepDyve free for 14 days.
N Roux, Y Bengio (2010)
Deep belief networks are compact universal approximatorsNeural Comput, 22
Z Guo, H Wang, J Yang, DJ Miller (2015)
A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural networkPLoS One, 10
E Guresen, G Kayakutlu, TU Daim (2011)
Using artificial neural network models in stock market index predictionExpert Syst Appl, 38
A Lendasse, E Bodt, V Wertz, M Verleysen (2000)
Non-linear financial time series forecasting-Application to the Bel 20 stock market indexEur J Econ Soc Syst, 14
JL Elman (1990)
Finding structure in timeCogn Sci, 14
YK Kwon, BR Moon (2007)
A hybrid neurogenetic approach for stock forecastingIEEE Trans Neural Netw, 18
H Larochelle, Y Bengio, J Louradour, P Lamblin (2009)
Exploring strategies for training deep neural networksJ Mach Learn Res, 10
GE Hinton, RR Salakhutdinov (2006)
Reducing the dimensionality of data with neural networksScience, 313
I Sutskever, GE Hinton (2008)
Deep, narrow sigmoid belief networks are universal approximatorsNeural Comput, 20
Z Zuo, G Wang (2014)
Learning discriminative hierarchical features for object recognitionIEEE Sig Proc Lett, 21
D Erhan, Y Bengio, A Courville, PA Manzagol, P Vincent, S Bengio (2010)
Why does unsupervised pre-training help deep learning?J Mach Learn Res, 11
GS Atsalakis, KP Valavanis (2009)
Surveying stock market forecasting techniques--Part II: soft computing methodsExpert Syst Appl, 36
CJ Huang, DX Yang, YT Chuang (2008)
Application of wrapper approach and composite classifier to the stock trend predictionExpert Syst Appl, 34
T Kuremoto, S Kimura, K Kobayashi, M Obayashi (2014)
Time series forecasting using a deep belief network with restricted Boltzmann machinesNeurocomputing, 137
J Patel, S Shah, P Thakkar, K Kotecha (2015)
Predicting stock market index using fusion of machine learning techniquesExpert Syst Appl, 42
LA Teixeira, ALI Oliveira (2010)
A method for automatic stock trading combining technical analysis and nearest neighbor classificationExpert Syst Appl, 37
W Shen, X Guo, C Wu, D Wu (2011)
Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithmKnowledge-Based Syst, 24
D Zhang, Z Zhou (2005)
(2D)2PCA : 2-Directional 2-Dimensional PCA for Efficient Face Representation and RecognitionNeurocomputing, 69
GE Hinton, S Osindero, Y-W Teh (2006)
A fast learning algorithm for deep belief netsNeural Comput, 18
H Situngkir, Y Surya (2004)
Neural network revisited: perception on modified Poincare map of financial time-series dataPhys A Stat Mech Appl, 344
J Patel, S Shah, P Thakkar, K Kotecha (2015)
Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniquesExpert Syst Appl, 42
Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D)2PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D)2PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN.
Multimedia Tools and Applications – Springer Journals
Published: Dec 17, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.