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Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price

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Abstract

Incurring the benefits of kernel type-based extreme learning machine (KELM), this paper proposes a Grey wolf optimization based multiquadratic kernel KELM (GWO-KELM) models for effective forecasting of agro-commodity prices such as Chana and Barley. The objective is to obtain best possible weight between hidden and output layer of an artificial neural network using GWO optimization techniques. These weights are used in KELM model to achieve improved performance. To assess the superiority performance of the proposed models, similar results are obtained using simulation study by simulating GA-KELM (genetic algorithm based KELM), PSO-KELM (particle swarm optimization based KELM) and GWO-KELM multiquadratic kernel types (GWO-KELM) models. From exhaustive simulation result, it is demonstrated that GWO-KELM forecasting model offers the best performance using three models in terms of matrices. The study pertains to short and long range prediction for prices of Chana and Barley using publicly available database.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Siksha ‘O’Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

    Nirjharinee Parida, Debahuti Mishra & Kaberi Das

  2. Department of Computer Application, National Institute of Technology, Raipur, C.G., India

    Narendra Kumar Rout

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  1. Nirjharinee Parida
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Parida, N., Mishra, D., Das, K. et al. Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price. Evol. Intel. 14, 529–544 (2021). https://doi.org/10.1007/s12065-019-00295-6

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