Machine Learning and Information Retrieval Techniques for Time Series Analysis - Rozin, Bionda; Pedronette, Daniel Carlos Guimarães (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI), 2025)
This work explores the intersection of Machine Learning and Information Retrieval for time series analysis, addressing key challenges in representation, classification, clustering, and retrieval. Four methodologies are proposed, covering univariate and multivariate time series across supervised, unsupervised, and semi-supervised scenarios. The approaches integrate contextual similarity learning, image-based representations, and domain-specific graph modeling, demonstrating competitive performance across multiple datasets.