Title: VANFIS: virtual adaptive neuro-fuzzy inference system for modelling and forecasting stock data

Authors: Sarat Chandra Nayak; Bijan Bihari Misra

Addresses: Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, 501401, India ' Department of Information Technology, Silicon Institute of Technology, Bhubaneswar, Odisha, 751024, India

Abstract: Random fluctuations take place in stock data. At fluctuation points, it is difficult to predict the next data points from the previous data. Close enough data points are more informative to the forecasting process but not available adequately. This article presents deterministic and stochastic methods for exploration of virtual data positions from actual data and incorporated to original dataset. An adaptive neuro-fuzzy inference system (ANFIS) is exposed to such virtual data positions and works in a virtual environment. The model is termed as VANFIS, i.e., virtual ANFIS and employed to infer future stock indices of real stock markets. The performance of VANFIS methods are validated using 15 years data from ten stock markets and using five metrics. Also, relative worth of proposed methods are carried out over ANFIS. Experimental results show the significant improvement in prediction accuracy when proposed methods adopted.

Keywords: stock market forecasting; financial time series forecasting; ANFIS; virtual data position; VDP; linear extrapolation.

DOI: 10.1504/IJBFMI.2019.101604

International Journal of Business Forecasting and Marketing Intelligence, 2019 Vol.5 No.2, pp.188 - 204

Received: 18 Feb 2019
Accepted: 06 Apr 2019
Published online: 13 Aug 2019 *

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