Financial Time Series Prediction Using Pelican Optimized Extreme Learning Machine with Reduced Weights

Peketi Syamala Rao, Gottumukkala Parthasaradhi Varma, Durga Prasad Chinta (), Kusuma Gottapu, Hyma Lakshmi Tv, Karanam Appala Naidu and Market Saritha
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Peketi Syamala Rao: SRKR Engineering College, Department of Information Technology
Gottumukkala Parthasaradhi Varma: KLEF Deemed to Be University
Durga Prasad Chinta: SRKR Engineering College, Department of Electrical and Electronics Engineering
Kusuma Gottapu: SRKR Engineering College, Department of Electrical and Electronics Engineering
Hyma Lakshmi Tv: SRKR Engineering College, Department of Electronics and Communication Engineering
Karanam Appala Naidu: Vignan’s Institute of Information Technology, Department of Electrical and Electronics Engineering
Market Saritha: Matrusri Engineering College, Department of Electrical and Electronics Engineering

Computational Economics, 2025, vol. 66, issue 6, No 11, 4763-4780

Abstract: Abstract This paper presents a new forecasting approach using the Pelican Optimized Extreme Learning Machine (PO-ELM) model, designed to enhance the prediction of future trends based on historical data. The PO-ELM model refines the ELM by introducing the pelican optimizer, which systematically identifies the decision parameters of the ELM like input weights and biases. In contrast to the conventional ELM, where weights are generated randomly, the PO-ELM method ensures that these parameters are optimized, leading to accurate and reliable forecasts. This enhancement significantly improves the model's predictive capabilities, especially for complex time series data like stock market information. The model is tested on real-time data, and improvements are observed in prediction accuracy via performance metrics such as root mean square error and coefficient of correlation. Additionally, the PO-ELM model achieves these improvements with a reduced number of weights and hidden layer neurons, demonstrating its efficiency in managing computational resources while maintaining high performance. The results underscore the potential of PO-ELM in delivering superior forecasting outcomes and comparisons are performed with ELM and radial basis function neural networks to show the advantages over other methods.

Keywords: Extreme learning machine; Financial time series; Pelican optimizer (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-025-10869-5

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