Machine Learning and Mathematical Modeling for Pattern Recognition in Large-Scale Numerical and Time-Series Datasets

Khin Myo Myo Minn, Hlaing Htake Khaung Tin, M. Sakthivanitha, J. Anciline Jenifer, I. Meireles Chrisostomo Faculty of Computing, University of Information Technology, Yangon, Myanmar. Faculty of Information Science, University of Information Technology, Yangon, Myanmar. Department of Information Technology, Vels Institute of Science Technology and Advance Studies, Chennai, Tamil Nadu, India. Department of Computer Applications, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India. Department of Mathematics, Federal University of Goiás, Catalão, Goiás, Brazil. 

Abstract: The fast advancements in the field of digital technologies have resulted in the generation of tremendous volumes of numerical and time-series data in different application fields, ranging from financial markets, medicine, climate research, to manufacturing processes and social networks. This paper presents how machine learning techniques can be combined with mathematical modelling for pattern recognition in large-scale numerical and time-series data sets. The paper proposes an integrated method that combines statistical, feature-extraction, and optimization techniques with machine learning prediction algorithms to achieve more accurate and efficient results in large-scale data analysis. Different machine learning approaches, both supervised and unsupervised, will be considered for identifying latent patterns, trends, anomalies, and dependencies in data sets. In addition, the study explores various preprocessing techniques, dimensionality reduction, and sequence modelling approaches to address the problem of noisy, voluminous data. It was established that combining mathematical models with machine learning leads to substantial improvements in accuracy, scalability, and efficiency for detecting hidden patterns in data. The results of the study show that the approach is well-suited for implementing real-time analytics and intelligent systems that support informed decision-making.

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Keywords: Machine Learning; Mathematical Modeling; Pattern Recognition; Time-Series Analysis; Large-Scale Datasets; Digital Technology; Social Networks; Prediction Algorithms; Intelligent Systems.

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Received on: 01/07/2025Revised on: 22/08/2025Accepted on: 15/09/2025Published on: 01/03/2026

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