In the fast-paced world, time-sensitive systems require low-latency predictions from continuous data streams. Traditional regression techniques struggle with dynamic data, leading to the need for novel methods. The proposed model uses R*-trees for granulation, iteratively forgetting outdated information to maintain recent relevant granules. Experiments show significant latency improvement and competitive prediction accuracy compared to state-of-the-art algorithms. The approach is amenable to integration with database systems, offering scalability and efficiency.
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by Niket Kathir... at arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.09588.pdfDeeper Inquiries