Title: Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model

Authors: Sibarama Panigrahi; H.S. Behera

Addresses: Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, 768018, India ' Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Odisha, 768018, India

Abstract: In this paper, we have proposed a multiplicative FLANN (MFLANN) model for time series forecasting. In addition, an improved version of teaching learning based optimisation (TLBO), called self-adaptive TLBO (SATLBO) has been proposed to train the MFLANN model. The proposed SATLBO uses the gradient descent learning algorithm in the teacher phase while uses the past experience to adapt the learners parameters. This unique integration of SATLBO with gradient descent learning algorithm is used to determine the near optimal weight set of MFLANN. The proposed method is implemented in MATLAB environment and the obtained results are compared with other methods (DE-based MFLANN, TLBO-based MFLANN, CRO-based MFLANN, Jaya-based MFLANN and ETLBO-JPSNN) considering 11 benchmark univariate time series datasets. Extensive statistical analysis on obtained results indicated that the proposed SATLBO-MFLANN method is better and statistically significant in comparison to its counterparts.

Keywords: functional link ANN; FLANN; multiplicative FLANN; MFLANN; teaching learning-based optimisation; TLBO; self-adaptive TLBO; SATLBO; hybrid model; time series forecasting; TSF.

DOI: 10.1504/IJCISTUDIES.2019.098013

International Journal of Computational Intelligence Studies, 2019 Vol.8 No.1/2, pp.4 - 26

Received: 17 Feb 2018
Accepted: 06 Jul 2018

Published online: 14 Feb 2019 *

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