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Article Open Access December 20, 2024

AI for Time Series and Anomaly Detection

Ravi Teja Avireneni 1,*, Sri Harsha Koneru 2, Naresh Kiran Kumar Reddy Yelkoti 3 and Sivaprasad Yerneni Khaga 4
1
Industrial Management, University of Central Missouri, USA
2
Computer Information Systems and Information Technology, University of Central Missouri, USA
3
Information Systems Technology and Information Assurance, Wilmington University, USA
4
Environmental Engineering, University of New Haven, USA
Publihed in: Current Research in Public Health (Volume 4, Issue 2, 2024)
Page(s): 120-131
DOI: 10.31586/jaibd.2024.1399
Received
September 20, 2024
Revised
October 29, 2024
Accepted
November 30, 2024
Published
December 20, 2024
Keywords
Time Series Forecasting; Anomaly Detection; Deep Learning; Multivariate Time Series; Artificial Intelligence; Real-Time Systems
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2024. Published by Scientific Publications
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Cite This Article

APA Style
Avireneni, R. T. , Avireneni, R. T. Koneru, S. H. , Koneru, S. H. Yelkoti, N. K. K. R. , & Yelkoti, N. K. K. R. (2024). AI for Time Series and Anomaly Detection. Current Research in Public Health, 4(2), 120-131. https://doi.org/10.31586/jaibd.2024.1399
ACS Style
Avireneni, R. T. ; Avireneni, R. T. Koneru, S. H. ; Koneru, S. H. Yelkoti, N. K. K. R. ; Yelkoti, N. K. K. R. AI for Time Series and Anomaly Detection. Current Research in Public Health 2024 4(2), 120-131. https://doi.org/10.31586/jaibd.2024.1399
Chicago/Turabian Style
Avireneni, Ravi Teja, Ravi Teja Avireneni. Sri Harsha Koneru, Sri Harsha Koneru. Naresh Kiran Kumar Reddy Yelkoti, and Naresh Kiran Kumar Reddy Yelkoti. 2024. "AI for Time Series and Anomaly Detection". Current Research in Public Health 4, no. 2: 120-131. https://doi.org/10.31586/jaibd.2024.1399
AMA Style
Avireneni RT, Avireneni RTKoneru SH, Koneru SHYelkoti NKKR, Yelkoti NKKR. AI for Time Series and Anomaly Detection. Current Research in Public Health. 2024; 4(2):120-131. https://doi.org/10.31586/jaibd.2024.1399 
@Article{crph1399,
AUTHOR = {Avireneni, Ravi Teja and Koneru, Sri Harsha and Yelkoti, Naresh Kiran Kumar Reddy and Khaga, Sivaprasad Yerneni},
TITLE = {AI for Time Series and Anomaly Detection},
JOURNAL = {Current Research in Public Health},
VOLUME = {4},
YEAR = {2024},
NUMBER = {2},
PAGES = {120-131},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1399},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2024.1399},
ABSTRACT = {Time series data are increasingly prevalent across domains such as finance, healthcare, manufacturing, and IoT, making accurate forecasting and anomaly detection critical for decision-making and system reliability. Traditional statistical methods (e.g., ARIMA, Holt-Winters) often fail to capture complex temporal dependencies and high-dimensional interactions inherent in modern time series. Recent advances in artificial intelligence particularly deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), temporal convolutional networks (TCNs), graph neural networks (GNNs) and Transformers have demonstrated marked improvements in modeling both univariate and multivariate series, as well as in detecting anomalies that deviate from learned norms (Darban, Webb, Pan, Aggarwal, & Salehi, 2022; Chiranjeevi, Ramya, Balaji, Shashank, & Reddy, 2024) [1,2]. Moreover, ensemble techniques and hybrid signal-processing + deep-learning pipelines show enhanced sensitivity and adaptability in real-world anomaly detection scenarios (Iqbal, Amin, Alsubaei, & Alzahrani, 2024) [3]. In this work, we provide a unified survey and comparative analysis of AI-driven time series forecasting and anomaly detection methods, highlight key industrial application domains, evaluate performance trade-offs (e.g., accuracy vs. latency, supervised vs. unsupervised learning), and discuss emerging challenges including interpretability, data drift, real-time deployment on edge devices, and integration of causal reasoning. Our findings suggest that while AI approaches significantly outperform classical techniques in many settings, careful consideration of data characteristics, evaluation metrics and deployment environment remains essential for effective adoption.},
}
%0 Journal Article
%A Avireneni, Ravi Teja
%A Koneru, Sri Harsha
%A Yelkoti, Naresh Kiran Kumar Reddy
%A Khaga, Sivaprasad Yerneni
%D 2024
%J Current Research in Public Health

%@ 2831-5162
%V 4
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%P 120-131

%T AI for Time Series and Anomaly Detection
%M doi:10.31586/jaibd.2024.1399
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1399

TY  - JOUR
AU  - Avireneni, Ravi Teja
AU  - Koneru, Sri Harsha
AU  - Yelkoti, Naresh Kiran Kumar Reddy
AU  - Khaga, Sivaprasad Yerneni
TI  - AI for Time Series and Anomaly Detection
T2  - Current Research in Public Health
PY  - 2024
VL  - 4
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SN  - 2831-5162
SP  - 120
EP  - 131
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1399
AB  - Time series data are increasingly prevalent across domains such as finance, healthcare, manufacturing, and IoT, making accurate forecasting and anomaly detection critical for decision-making and system reliability. Traditional statistical methods (e.g., ARIMA, Holt-Winters) often fail to capture complex temporal dependencies and high-dimensional interactions inherent in modern time series. Recent advances in artificial intelligence particularly deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), temporal convolutional networks (TCNs), graph neural networks (GNNs) and Transformers have demonstrated marked improvements in modeling both univariate and multivariate series, as well as in detecting anomalies that deviate from learned norms (Darban, Webb, Pan, Aggarwal, & Salehi, 2022; Chiranjeevi, Ramya, Balaji, Shashank, & Reddy, 2024) [1,2]. Moreover, ensemble techniques and hybrid signal-processing + deep-learning pipelines show enhanced sensitivity and adaptability in real-world anomaly detection scenarios (Iqbal, Amin, Alsubaei, & Alzahrani, 2024) [3]. In this work, we provide a unified survey and comparative analysis of AI-driven time series forecasting and anomaly detection methods, highlight key industrial application domains, evaluate performance trade-offs (e.g., accuracy vs. latency, supervised vs. unsupervised learning), and discuss emerging challenges including interpretability, data drift, real-time deployment on edge devices, and integration of causal reasoning. Our findings suggest that while AI approaches significantly outperform classical techniques in many settings, careful consideration of data characteristics, evaluation metrics and deployment environment remains essential for effective adoption.
DO  - AI for Time Series and Anomaly Detection
TI  - 10.31586/jaibd.2024.1399
ER  -