<ul data-eligibleForWebStory="true"><li>Real-time network traffic forecasting is essential for network management and resource allocation.</li><li>Existing approaches assume full network traffic data, but practical scenarios often have missing data.</li><li>A generative model approach is proposed for real-time network traffic forecasting with missing data.</li><li>The approach models forecasting as a tensor completion problem and incorporates a pre-trained generative model for low-rank structure.</li><li>The generative model captures the low-rank structure of network traffic data, simplifying the optimization process.</li><li>Optimization is done on the latent representation rather than the high-dimensional tensor.</li><li>A theoretical recovery guarantee quantifies the error bound of the proposed approach.</li><li>Experiments on real-world datasets show accurate network traffic forecasting within 100 ms with a MAE below 0.002.</li></ul>

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach

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