<ul><li>Time series classification is a crucial task in healthcare and industry, hindered by limited time series foundation models (TSFMs) due to lack of datasets.</li><li>A new framework called Time Vision Transformer (TiViT) is introduced, converting time series data into images to utilize pretrained Vision Transformers (ViTs) from image datasets.</li><li>Theoretical analysis shows that patching ViTs for time series can enhance label-relevant tokens and decrease sample complexity.</li><li>TiViT achieves top performance on time series benchmarks by leveraging hidden representations from large OpenCLIP models, emphasizing the effectiveness of intermediate layers for classification.</li></ul>

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

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