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Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
Annita Vapsi, Penghang Liu, Saheed Obitayo +8
Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel…
TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure
Maxime Kawawa-Beaudan, Srijan Sood, Kassiani Papasotiriou +2
Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, a 524M-…
LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
Elizabeth Fons, Alejandro Sztrajman, Yousef El-Laham +3
Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Trans…
Mixup Regularization: A Probabilistic Perspective
Yousef El-Laham, Niccolò Dalmasso, Svitlana Vyetrenko +2
In recent years, mixup regularization has gained popularity as an effective way to improve the generalization performance of deep learning models by training on convex combinations…