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Introduces FreSH, a frequency-segmented hierarchical multi-expert framework for multivariate time series classification, achieving state-of-the-art accuracy on UEA benchmarks with reduced model size and computational cost.
This paper presents a dependency-aware autoscaling framework for serverless environments, integrating graph-based bottleneck identification, multi-model forecasting (MLP, LSTM, CNN) via a probabilistic ensemble, and cost-aware scaling control. Experiments show 99.88% prediction accuracy and reduced infrastructure costs.