Advanced Web Traffic Modelling and Forecasting with a Hybrid Predictive Approach

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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Publication Details

Journal
Journal of Web Engineering
Published
2025-06-24
DOI
https://doi.org/10.13052/jwe1540-9589.2434
Citations
1
Primary Topic
Advanced Clustering Algorithms Research
Type
article
Field-Weighted Citation Impact
2.48

Funders

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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.

Journal of Web Engineering
Princess Nourah bint Abdulrahman University (SA), Northern Border University (SA), China Medical University (TW), Symbiosis International University (IN), China Medical University Hospital (TW), Metropolitan University (BD), University of Business and Technology (SA), University of Petroleum and Energy Studies (IN), Punjab Engineering College (IN), Hong Kong Metropolitan University (HK), University of Hong Kong (HK)
Princess Nourah Bint Abdulrahman University, National Science and Technology Council
Openalex Percentile: Top 10%
Advanced Clustering Algorithms Research
2.48
DOIOpen Access PDFOpenAlex
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