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Synthetic Time Series Generation via Complex Networks

Vale, Jaime et al.
arXiv (All) · Papers
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machine-learning
Time series data are essential for a wide range of applications, yet access to high-quality datasets is often constrained by privacy concerns, acquisition costs, and labelling challenges. Synthetic time series generation has emerged as a promising approach to address these limitations. In this work,
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