AI RESEARCH
SEAnet: A Deep Learning Architecture for Data Series Similarity Search
arXiv CS.LG
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ArXi:2603.01448v2 Announce Type: replace-cross A key operation for massive data series collection analysis is similarity search. According to recent studies, SAX-based indexes offer state-of-the-art performance for similarity search tasks. However, their performance lags under high-frequency, weakly correlated, excessively noisy, or other dataset-specific properties. In this work, we propose Deep Embedding Approximation (DEA), a novel family of data series summarization techniques based on deep neural networks.