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Forecasting of mobile network traffic and spatio–temporal analysis using modLSTM

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Abstract

This paper introduces an innovative system and prediction model for forecasting network traffic in specific geographical locations using historical data. As Internet service providers increasingly rely on data analytics for decision-making, optimized network forecasting faces challenges such as data cleaning and preprocessing. Our approach utilizes an Artificial Recurrent Neural Network-based Modified Long Short-Term Memory model to provide continuous and precise predictions of network traffic. Notably, the proposed model outperforms conventional LSTM models, achieving a 61.9% reduction in Mean Absolute Percent Error. Our approach also integrates an interpolation technique to address the zero-component error. This further enhances the effectiveness and reliability of the model. The model promises to enhance resource utilization and lighten the load on traffic resource provisioning entities, promoting more efficient mobile network traffic management. The low training time of 3.26 min and prediction time of 0.14 s pave the way for real-time implementation of the model for network traffic forecasting and management. The comparative analysis with state-of-the-art models proves the supremacy of the proposed model.

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Availability of data and material

The dataset used in this research is available online. The references and citations are provided in this manuscript.

Code availability

The code is available with the authors. They will upload the modular and executable code to an open-source platform after the completion of the review process.

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Acknowledgements

We acknowledge Manipal University Jaipur for providing the IoT and Machine Learning Lab (114-2AB) for completing the research work presented in this manuscript.

Funding

The research presented in this manuscript is supported by SIMPATICO_ZUMPANO-Progetto di Ricerca POR SIMPATICO: Sistema Informativo Medico Patologie Complesse 3D.

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Author notes

  1. Vidyadhar J. Aski, Rugved Sanjay Chavan, Geeta Rani, Ester Zumpano and Eugenio Vocaturo have contributed equally to this work.

Authors and Affiliations

  1. Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India

    Vidyadhar J. Aski, Rugved Sanjay Chavan, Vijaypal Singh Dhaka & Geeta Rani

  2. Emerging Technology CoE, Enterprise Applications, Mahindra Digital Engine, Mahidnra and Mahindra Ltd, Mumbai, Maharashtra, India

    Vidyadhar J. Aski

  3. Department of Computer Engineering, Modeling, Electronics and Systems (DIMES), University of Calabria, Rende(Cosenza), Italy

    Ester Zumpano & Eugenio Vocaturo

  4. Computer Science, University of Virginia, Charlottesville, USA

    Rugved Sanjay Chavan

  5. National Research Council, Institute of Nanotechnology (NANOTEC), Rende(Cosenza), Italy

    Ester Zumpano & Eugenio Vocaturo

Authors
  1. Vidyadhar J. Aski
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  2. Rugved Sanjay Chavan
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  3. Vijaypal Singh Dhaka
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  4. Geeta Rani
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  5. Ester Zumpano
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  6. Eugenio Vocaturo
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Contributions

All the authors have equal contributions to this research and the preparation of the manuscript.

Corresponding author

Correspondence to Geeta Rani.

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The authors declare that there is no conflict of interest associated with this research.

Ethical approval

The experiments were performed on data not related to living beings. No human beings and animals were involved in conducting the experiments. Thus, approval from an ethical committee is not required.

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No living beings are involved in conducting the experiments for this research. Thus, consent to participate is not required.

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The authors involved in conducting this research give their consent for the publication of the article titled “Forecasting and Spatio–Temporal Analysis of Mobile Network Traffic using modLSTM”.

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Editors: Dino Ienco, Roberto Interdonato, Pascal Poncelet.

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Aski, V.J., Chavan, R.S., Dhaka, V.S. et al. Forecasting of mobile network traffic and spatio–temporal analysis using modLSTM. Mach Learn 113, 2277–2300 (2024). https://doi.org/10.1007/s10994-023-06471-1

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  • DOI: https://doi.org/10.1007/s10994-023-06471-1

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