Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model. (2019)

• Record Type: Journal Article
• Authors: Panigrahi, Sibarama; Behera, H.S.
• Is Part Of: International journal of computational intelligence studies. Volume 8:Number 1/2(2019)
• Page Start: 4
• Page End: 26
• Publication Date: 2019
• Subjects: functional link ANN -- FLANN -- multiplicative FLANN -- MFLANN -- teaching learning-based optimisation -- TLBO -- self-adaptive TLBO -- SATLBO -- hybrid model -- time series forecasting -- TSF; Computational intelligence -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-4985

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.