Articles

Selected Research

Selected publications spanning computational optimization, multiresolution time-series forecasting, neural networks, and applied predictive modeling.

Articles

Selected Research

Selected publications spanning computational optimization, multiresolution time-series forecasting, neural networks, and applied predictive modeling.

Article

An Improved Iterative Method for Solving General System of Equations via Genetic Algorithms

International Journal of Information Technology, Modeling and Computing, 2016

This research introduces an improved iterative approach for solving general systems of equations by combining residual minimization with genetic algorithms. The work explores how evolutionary optimization can extend iterative numerical methods to more complex and large-scale systems while providing a foundation for parallel computation.

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Article

Improving DWT-RNN Model via B-Spline Wavelet Multiresolution to Forecast a High-Frequency Time Series

Expert Systems with Applications, 2019

This research develops a multiresolution forecasting architecture that combines B-spline discrete wavelet transforms with recurrent neural networks. By decomposing high-frequency time series into multiple resolution levels before prediction, the model improves the ability of neural networks to capture complex temporal behavior and produces more accurate forecasts across the evaluated financial time series.

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Article

Multi-WRNN Model for Pricing the Crude Oil Futures Market

Expert Systems with Applications, 2021

This research moves beyond forecasting crude oil prices from historical price patterns alone. It incorporates key factors from the broader oil-market system including variables related to supply, demand, production, and market dynamics into a multi-factor wavelet-based recurrent neural network for futures pricing and forecasting. By combining operational market context with multiresolution signal decomposition and deep learning, the work represents an early step toward system-aware forecasting: predicting not only from what happened in the signal, but from the underlying system and processes that influence it.

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