Time series foundation models can match specialized methods without attention mechanisms or complex tokenization—a simple convolutional encoder with dual-view inputs and synthetic pre-training is sufficient.
Researchers adapted TS2Vec, a self-supervised time series method, into a reusable foundation model by pre-training on synthetic data, using dual input views (raw amplitude and normalized shape), and distilling two specialist encoders into one.