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Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices
2017
Ain Shams Engineering Journal
The underlying system models of time series prediction are complex and not known a priori, hence, accurate and unbiased estimation cannot be always achieved using well known linear techniques. The estimation process requires more advanced prediction algorithms, such as multilayer perceptrons (MLPs). This paper presents an artificial chemical reaction neural network (ACRNN), which uses artificial chemical reaction optimization (ACRO) to train the MLP models for forecasting the stock market
doi:10.1016/j.asej.2015.07.015
fatcat:6vfz4cj6xbg2lj2dwqjqt37cba