Large vision models slightly beat strong baselines on imaged time series classification, but their forecasting advantage is narrow, tied to periodic patterns, and shrinks with long histories.
TimEHR: Image-based Time Series Generation for Electronic Health Records
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abstract
Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper, we propose a novel generative adversarial network (GAN) model, TimEHR, to generate time series data from EHRs. In particular, TimEHR treats time series as images and is based on two conditional GANs. The first GAN generates missingness patterns, and the second GAN generates time series values based on the missingness pattern. Experimental results on three real-world EHR datasets show that TimEHR outperforms state-of-the-art methods in terms of fidelity, utility, and privacy metrics.
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From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?
Large vision models slightly beat strong baselines on imaged time series classification, but their forecasting advantage is narrow, tied to periodic patterns, and shrinks with long histories.