What are the main limitations of image-based contrastive learning methods like MoCo and SimCLR when applied to multivariate time-series data?

Image-based contrastive methods like MoCo and SimCLR do not naturally extend to multivariate time-series because they were developed for visual data and are not designed to handle the temporal structures and multi-sensor spatial correlations inherent in multivariate time-series.

The source notes that MoCo, SimCLR, and other image-based contrastive learning methods were primarily developed for visual data and achieve strong performance on vision tasks. However, multivariate time-series data contain both temporal structures and multi-sensor spatial correlations, and these are characteristics that image-based contrastive learning frameworks are not designed to handle. Consequently, such methods require adaptation or specialized frameworks to capture temporal dynamics and relationships among sensors, rather than being directly applicable to multivariate time-series classification.