Time series prediction of stock price using deep belief networks with intrinsic plasticity
- 1 May 2017
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2017 29th Chinese Control And Decision Conference (CCDC)
- p. 1237-1242
- https://doi.org/10.1109/ccdc.2017.7978707
Abstract
In recent years, the stock market plays an important role, which has attracted more and more attentions. The key problem of the stock market prediction is how to design a method to improve the prediction performance. As we know, the biggest challenge is that the stock time series is essentially dynamic, nonlinear, complicated, nonparametric and chaotic. In this paper, we propose a novel method to predict the stock closing price based on the deep belief networks (DBNs) with intrinsic plasticity. In the experiments, the stock in S&P 500 is used to examine the performance. The back propagation algorithm is used for output training to make minor adjustments of structure parameters. The intrinsic plasticity (IP) is also applied into the network to make it have adaptive ability. It is believed that IP learning for adaptive adjustment of neuronal response to external inputs is beneficial for maximizing the input-output mutual information. Our results show that the application of IP learning can remarkably improve the prediction performance. Moreover, the effects of two kinds of IP rules on the performance of prediction are examined. Compared with Triesch's IP and without IP, DBN with Li's IP learning has much better prediction performance than the others. These results may have important implications on the modeling of neural network for complex time series prediction.Keywords
This publication has 19 references indexed in Scilit:
- Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask LearningIEEE Transactions on Intelligent Transportation Systems, 2014
- Predicting Asset Value through Twitter BuzzPublished by Springer Science and Business Media LLC ,2012
- Deep, Big, Simple Neural Nets for Handwritten Digit RecognitionNeural Computation, 2010
- Homeostatic plasticity and STDP: keeping a neuron's cool in a fluctuating worldFrontiers in Synaptic Neuroscience, 2010
- Learning Deep Architectures for AIFoundations and Trends® in Machine Learning, 2009
- Synergies Between Intrinsic and Synaptic Plasticity MechanismsNeural Computation, 2007
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Introduction to financial forecastingApplied Intelligence, 1996
- A clustering technique for digital communications channel equalization using radial basis function networksIEEE Transactions on Neural Networks, 1993