Title: Ordered weighted averaging operator used to enhance the accuracy of fuzzy predictor based on genetic algorithm

Authors: Rohit Garg; Md. Tabrez Nafis; Bindu Garg

Addresses: Department of Computer Science, Jamia Hamdard University, New Delhi 110062, India ' Department of Computer Science, Jamia Hamdard University, New Delhi 110062, India ' Department of Computer Engineering, Bharati Vidyapeeth Deemed University College of Engineering, Pune, India

Abstract: In this paper, we have proposed a novel concept to optimise ordered weighted aggregation (OWA) based fuzzy time series predictor (FTSP) using genetic algorithm (GA). Firstly, accurateness of FTSP is enhanced by applying effective method of aggregation on past observations using OWA weights. These weights are determined on the basis of importance of fuzzy set in the system by employing regularly increasing monotonic (RIM) quantifiers. Subsequently, GA is used to optimise membership functions of FTSP by generating its wide range of parameters in the region of time series. Lastly, this model is capable of controlling its performance by varying GA parameters. To assess proposed method, we used dataset of enrolments and outpatient visits, as used by almost all previous research in this domain. Evaluation results indicate coalescing OWA and GA for FTSP significantly reduced mean square error (MSE) and average forecasting error rate (AFER).

Keywords: time series analysis; fuzzy logic; GA; genetic algorithm; optimisation; OWA; ordered weighted aggregation.

DOI: 10.1504/IJISTA.2018.091602

International Journal of Intelligent Systems Technologies and Applications, 2018 Vol.17 No.1/2, pp.229 - 253

Received: 08 Apr 2017
Accepted: 30 Aug 2017
Published online: 08 May 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article