DAD4TS trains a diffusion-based generator jointly with a forecaster under RL control and geometric projections to produce augmentation samples that boost accuracy on small-scale time-series data, with validation reported on five of six real-world datasets.
Reaugment: Model zoo-guided rl for few-shot time series augmentation and forecasting
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CSRA applies input-adaptive spectral residual perturbations to multi-system ICU time series, trained end-to-end with the predictor and consistency losses, yielding 10.2% MSE and 3.7% MAE reductions on MIMIC-IV sepsis prediction.
citing papers explorer
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DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
DAD4TS trains a diffusion-based generator jointly with a forecaster under RL control and geometric projections to produce augmentation samples that boost accuracy on small-scale time-series data, with validation reported on five of six real-world datasets.
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CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction
CSRA applies input-adaptive spectral residual perturbations to multi-system ICU time series, trained end-to-end with the predictor and consistency losses, yielding 10.2% MSE and 3.7% MAE reductions on MIMIC-IV sepsis prediction.