Abstract
Time-series analysis is a critical task in various fields, such as finance, economics, and environmental monitoring, where data is collected over time. However, many time-series datasets exhibit stochastic variability, making it challenging to identify and characterize patterns accurately. Traditional time-series analysis techniques may fail to account for this variability, leading to inaccurate results. This paper presents an innovative approach that integrates several techniques from statistics, signal processing, and machine learning to provide a comprehensive and accurate analysis of time-varying patterns in data. Our approach includes pre-processing steps to remove noise and outliers, followed by a feature extraction stage to identify relevant features in the data. We then apply a machine learning algorithm to model the underlying patterns and capture the stochastic variability. We validate our method on several real-world time-series datasets, including financial market data and environmental sensor data. Our results show that our approach outperforms traditional time-series analysis techniques and provides more accurate and comprehensive insights into the underlying patterns in the data. We believe that our approach has significant potential for applications in various domains, including finance, environmental monitoring, and healthcare.
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FK, Kamalakannan, and SSA contributed equally to this work. FK and Kamalakannan conducted the experiments, analyzed the results, and wrote the initial manuscript. SSA reviewed and edited the manuscript and helped in the revision process, providing valuable feedback and suggestions for improvement. All authors discussed the results, interpreted the findings, and approved the final version of the manuscript.
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Khan, A.B.F., Kamalakannan, K. & Ahmed, N.S.S. Integrating Machine Learning and Stochastic Pattern Analysis for the Forecasting of Time-Series Data. SN COMPUT. SCI. 4, 484 (2023). https://doi.org/10.1007/s42979-023-01981-0
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DOI: https://doi.org/10.1007/s42979-023-01981-0