Title: A self-adaptive fuzzy-based optimised functional link artificial neural network model for financial time series prediction
Authors: Soumya Das; Abhimanyu Patra; Sarojananda Mishra; Manas Ranjan Senapati
Addresses: Department of Computer Science and Information Technology, Government College of Engineering, Kalahandi 766002, Odisha, India ' Department of Computer Science Engineering, Gandhi Institute for Education and Technology, Baniatangi, Khurdha 752060, Odisha, India ' Department of Computer Science and Engineering, Indira Gandhi Institute of Technology, Saranga 759146, Odisha, India ' Department of Computer Science and Engineering, Centurion Institute of Technology, Centurion University of Technology and Management, Khurda 752050, Odisha, India
Abstract: In recent years, new data mining and machine learning techniques have been developed and applied to various fields of science. Out of these recently developed techniques few offer online support and are able to adapt to large and complex financial dataset. Therefore, the present research adopts Functional Link Artificial Neural Network (FLANN) model for predicting the closing price of three companies namely Yahoo Inc, Nokia and Bank of America. The FLANN model used is trained by fuzzy after normalisation of the data and closing price is forecasted for one day and one week ahead. The prediction result is compared with the parameters of the FLANN model trained by Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The proposed training method provides better accuracy and takes less time as compared to training the FLANN model using PSO or GA. The proposed approach has also been compared with a linear dataset for validation. The FLANN-fuzzy approach is seen to provide better results in predicting financial distress.
Keywords: FLANN; functional link ANNs; artificial neural networks; PSO; particle swarm optimisation; genetic algorithms; neuro-fuzzy; fuzzy logic; MSE; mean square error; RMSE; root mean square error; MAPE; mean absolute percentage error; financial time series; financial prediction; closing price forecasting; closing prices; stock market prices; financial distress.
International Journal of Business Forecasting and Marketing Intelligence, 2015 Vol.2 No.1, pp.55 - 77
Received: 31 Oct 2015
Accepted: 25 Jan 2016
Published online: 14 Mar 2016 *