Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7210854 | Ain Shams Engineering Journal | 2017 | 20 Pages |
Abstract
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 indices. The underlying motivation for using ACRO is the ability to overcome the issues of convergence, parameter setting and overfitting and to accurately forecast financial time series data even when the underlying system processes are typically nonlinear. Historical data of seven different stock indices have been collected for 15Â years to test the performance of the ACRNN approach. After extensive experimentation, it is observed that the ACRNN technique demonstrates significant improvements in prediction accuracy over the MLP approach.
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Engineering (General)
Authors
S.C. Nayak, B.B. Misra, H.S. Behera,