Optimizing Big Data Traffic Prediction Using Generalizations of Choquet Integral with Adaptive Weighting

Managing big data traffic plays an important role in contemporary communication and is essential for efficiently handling an unprecedented volume of information. In the globalized context of the internet, the ability to measure and predict this traffic is a strategically valuable resource that requires a deep understanding of historical data. This article proposes a predictor based on a generalization of the Choquet integral, which aggregates data, reducing the complexity and dimensionality of traffic predictions. The approach is assessed using real data, demonstrating that the Choquet integral achieves higher accuracy with the appropriate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> parameter. Considering the worst-case scenario in terms of wasted time, and given that the overall algorithm achieved a satisfactory error rate, we can conclude that the least efficient algorithm was the brute force search. In comparison, binary search and random binary search demonstrated a time efficiency improvement of 56.75 % and 59.49 %, respectively. Among the integrals evaluated, the Choquet (a) integral yielded the smallest errors.