REAL-TIME ANOMALY DETECTION IN STREAMING TIME SERIES DATA USING LIGHTWEIGHT MACHINE LEARNING MODEL

Registration ID: IJNRD_302600

Published ID: IJNRD2412098

: http://doi.one/10.1729/Journal.42596

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MD MEHEDI HASAN JONY

Real-Time Anomaly Detection, Streaming Time Series Data, Adaptive Anomaly Detection, Lightweight machine Learning, Model

The extended usage of streaming time series data especially in financial businesses, health care, manufacturing, and IoT has augmented the need to have instant anomaly detection services. Since real-time detection of anomalies plays a crucial role in maximizing the reliability and safety of the systems, none of the conventional statistical and machine learning models are suitable for the efficient real-time analysis of streaming data in terms of required time, computing power, and memory occupation. Such methods often need massive computational resources and do not take into account the changes in data distribution and level of noise. To tackle these challenges, this work presents a new concept of lightweight machine learning model that achieves both high detection rate and low computational requirements. Based on the Hoeffding Tree algorithm that allows updating the decision rules with new data that flows in when building up the model and incorporating a wide range of adaptive learning techniques, the model is designed to be as effective in real time anomaly detection mode. It is required to be adaptive to growth patterns of data, to noisy environment, and to restricted resources but being scalable and low latency at the same time. To assess the performance of the proposed model, comprehensive tests were carried out on various widely available datasets while considering the utilization of fully controlled noise and presence of concept drift. The results again and again prove the effectiveness of using the model in maintaining high detection accuracy despite the noise added and fast shifts in data context. The proposed model proved to be considerably better than traditional methods of anomaly detection in terms of computational overhead, delay, and scalability for stream processing. It is best suited to the real-world applications such as IoT monitoring, Industries 4.0 automation, edge computing where computational power is scarce while real-time processing is paramount. This work fills the existing gap between high accuracy and practical scalability of the anomaly detection task and will serve as a solid base for further enhancement of lightweight machine learning techniques for the state stream data.

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MD MEHEDI HASAN JONY (December-2024). REAL-TIME ANOMALY DETECTION IN STREAMING TIME SERIES DATA USING LIGHTWEIGHT MACHINE LEARNING MODEL. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(12), a912-a928. http://doi.one/10.1729/Journal.42596

Volume 9 Issue 12, December-2024

Pages : a912-a928

Paper Reg. ID: IJNRD_302600

Published Paper Id: IJNRD2412098

Research Area: Science and Technology

Author Type: Foreign Author

Country: Sydney, New South Wales, Austria

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Crossref DOI: http://doi.one/10.1729/Journal.42596

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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