Title :
Application of Improved LM-BP Neuron Network in stock prediction
Author :
Lei Zhang ; Bofeng Zhang ; Yajun Gu
Author_Institution :
Comput. Dept., Shanghai TV Univ., Shanghai, China
Abstract :
The prediction of stock closing quotation is very difficult because the time series seem random but not random completely. So a Back-Propagation Neurons Network based on Improved Levenberg-Marquardt algorithm was presented in this paper. The nonlinear mapping characteristic of the Back-Propagation Neural Network makes it can approach to every function in any precision. The Improved Levenberg-Marquardt algorithm can accelerate the speed of convergence, so it is easy to approach to the global optimal solution, not the local optimal solutions and. The prediction experiment shows that the Improved Levenberg-Marquardt algorithm works better than traditional Auto-regressive Moving-Average model.
Keywords :
autoregressive moving average processes; backpropagation; financial data processing; neural nets; stock markets; Levenberg-Marquardt backpropagation; autoregressive moving-average model; convergence speed; global optimal solution; improved LM-BP neuron network; improved Levenberg-Marquardt algorithm; local optimal solutions; nonlinear mapping characteristic; stock closing quotation prediction; time series; Back-Propagation Neurons Network; Levenberg-Marquardt algorithm; Stock Prediction;
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4673-2963-7
DOI :
10.1109/ICCSNT.2012.6526238