Advanced Web Traffic Modelling and Forecasting with a Hybrid Predictive Approach

Authors

  • Ujjwal Thakur Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India
  • Sunil K. Singh Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India
  • Sudhakar Kumar Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India https://orcid.org/0000-0001-7928-4234
  • Harmanjot Singh Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India
  • Varsha Arya Hong Kong Metropolitan University (HKMU), Hong Kong, SAR, China, Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India
  • Brij B. Gupta Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan, China, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan ,Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India ,University of Economics and Human Science, Warsaw, Poland
  • Razaz Waheeb Attar Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
  • Ahmed Alhomoud Department of Computer Science, College of Science, Northern Border University, Arar 91431, Saudi Arabia https://orcid.org/0000-0003-2782-3106
  • Kwok Tai Chui Hong Kong Metropolitan University (HKMU), Hong Kong, SAR, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2434

Keywords:

Web Traffic Analysis, ARIMA, LSTM, prophet model, time series forecasting, predictive analytics, user engagement, seasonal variations, machine learning

Abstract

Web traffic analysis is crucial for optimising user experience and engagement. This research explores a hybrid approach combining traditional statistical methods, like the autoregressive integrated moving average (ARIMA) model, with advanced techniques such as long short-term memory (LSTM) neural networks and the Prophet model. ARIMA effectively captures linear trends, seasonal effects, and cyclic behaviours, while LSTM handles complex non-linear patterns, and Prophet addresses seasonal variations and missing data. The hybrid model demonstrated 93% accuracy in predicting web traffic, highlighting the benefits of integrating these methodologies. This approach enables businesses to better manage resources, boost user engagement, and improve revenue. Future research will focus on refining hybrid models by incorporating new data features and ensemble methods to further enhance prediction accuracy, ultimately advancing the understanding of web traffic trends and user behaviour.

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Author Biographies

Ujjwal Thakur, Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India

Ujjwal Thakur is a dedicated researcher and writer specializing in machine learning, web analytics, and computational statistics. He is currently pursuing a Bachelor of Engineering in Computer Science and Engineering at Chandigarh College of Engineering and Technology, Sector-26, Chandigarh. Ujjwal combines academic rigor with a keen interest in real-world problem-solving. Through his writing, he aims to inform, inspire, and engage readers by bridging the gap between complex theories and their practical implementations.

Sunil K. Singh, Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India

Sunil K. Singh is working as a Professor and Head, Department of Computer Science & Engineering at Chandigarh College of Engineering and Technology (CCET) Degree Wing, Chandigarh. His areas of expertise are high-performance computing, LINUX/UNIX, AI, natural language processing (NLP), machine learning, Internet of Things (IoT), computer networks, microprocessors, and embedded systems. He has published more than 190+ research papers in reputed international/national journals, conferences, and workshops. He has 07 patents granted and 02 patents published, and some are in the pipeline too. His textbook, titled “Linux Yourself: Concept & Programming”, was published by Taylor and Francis (CRC Press) in August 2021.

Sudhakar Kumar, Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India

Sudhakar Kumar is working in the post of Assistant Professor (CSE) in the Chandigarh College of Engineering and Technology, Sector 26, Chandigarh (under Chandigarh UT Administration). He pursued his Ph.D. degree in high-performance computing (algorithm) and compiler optimization from Panjab University, Chandigarh, India. He has gained a Master’s degree in Technology (M. Tech.) from the Indian Institute of Technology (IIT), Guwahati. His areas of specialization are high-performance computing, compiler optimization, machine learning, artificial intelligence, and human–computer interaction. He has published more than 80+ research papers in different journals, conferences, and book chapters, and 2 design patents.

Harmanjot Singh, Department of CSE, Chandigarh College of Engineering & Technology, Chandigarh, India

Harmanjot Singh is a dedicated researcher and writer with a focus on machine learning, web analytics, and computational statistics. Their work explores critical perspectives and advancements in these fields. Passionate about knowledge and innovation, they aim to contribute meaningful insights. Harmanjot is currently pursuing a B.Eng. in Computer Science and Engineering at Chandigarh College of Engineering and Technology, Sector-26, Chandigarh. Through their writing, they seek to inform, inspire, and engage readers.

Varsha Arya, Hong Kong Metropolitan University (HKMU), Hong Kong, SAR, China, Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India

Varsha Arya is a researcher affiliated with Hong Kong Metropolitan University (HKMU), Hong Kong. She holds a master’s degree in business administration from Rajasthan University, Jaipur, India, and is currently pursuing a Ph.D. at Asia University, Taichung, Taiwan. With over seven years of research experience, she has published more than 25 papers in top journals and conferences. Her research interests span business administration, technology management, cyber–physical systems, cloud computing, healthcare, and networking. She has made significant contributions to deep learning, machine learning, IoT, cybersecurity, and smart city technologies, with a focus on intrusion detection, federated learning, and 6G networks.

Brij B. Gupta, Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan, China, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan ,Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India ,University of Economics and Human Science, Warsaw, Poland

Brij B. Gupta received his PhD degree from the Indian Institute of Technology (IIT) Roorkee, India. With over 19 years of professional experience, he has made significant contributions to the fields of cybersecurity, cloud computing, artificial intelligence, blockchain technologies, and social media networking. His extensive research portfolio includes over 500 publications in esteemed journals and conferences, alongside 35 authored books and 10 patents, accumulating more than 35000 citations. Dr. Gupta is a Senior Member of IEEE and ACM. Currently, Dr. Gupta is the Director of the International Center for AI and Cyber Security Research and Innovations at Asia University, Taiwan, where he continues to drive research excellence and innovation in the field of cybersecurity and emerging technologies. His work continues to influence academia and industry, advancing knowledge in cybersecurity and intelligent computing systems.

Razaz Waheeb Attar, Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Razaz Waheeb Attar is a researcher in the Management Department at the College of Business Administration, Princess Nourah bint Abdulrahman University, Saudi Arabia. Her research focuses on statistical modelling, social media analytics, and emerging technologies such as the Internet of Things (IoT). She has contributed to studies on composite reliability, convergent validity, and model performance, employing structural equation modelling (SEM) to assess social media platforms and networking sites. Additionally, her work explores the role of social media in various contexts, including its impact on air pollution and data accuracy.

Ahmed Alhomoud, Department of Computer Science, College of Science, Northern Border University, Arar 91431, Saudi Arabia

Ahmed Alhomoud is an assistant professor in the Department of Computer Science at Northern Border University, Saudi Arabia. He earned his Ph.D. in Computer Science from the University of Southampton, UK. His research interests span digital forensics, cyber security, the Internet of Things (IoT), and blockchain technology. Dr. Alhomoud has contributed extensively to topics such as data privacy, machine learning, decision trees, and healthcare data security. His work also focuses on optimizing model performance, improving detection accuracy, and enhancing interoperability in IoT devices, particularly within the healthcare sector and smart city development.

Kwok Tai Chui, Hong Kong Metropolitan University (HKMU), Hong Kong, SAR, China

Kwok Tai Chui received a B.Eng. degree in Electronic and Communication Engineering – Business Intelligence Minor, with first-class honours, and a Ph.D. degree in Electronic Engineering from City University of Hong Kong. He has industry experience as Senior Data Scientist in an Internet of Things (IoT) company. He joined the School of Science and Technology at the Hong Kong Metropolitan University as a Research Assistant Professor.

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Published

2025-06-24

How to Cite

Thakur, U. ., Singh, S. K. ., Kumar, S. ., Singh, H. ., Arya, V. ., Gupta, B. B. ., Attar, R. W. ., Alhomoud, A. ., & Chui, K. T. . (2025). Advanced Web Traffic Modelling and Forecasting with a Hybrid Predictive Approach. Journal of Web Engineering, 24(03), 409–456. https://doi.org/10.13052/jwe1540-9589.2434

Issue

2025: Vol 24 Iss 3

Section

Articles