What is the primary motivation for using Contrastive Learning (CL) in multivariate time-series classification, according to the chapter?

The primary motivation for using Contrastive Learning (CL) in multivariate time-series classification is to reduce the heavy reliance on large amounts of manually labeled data, which is labor-intensive and often infeasible at scale. CL learns from unlabeled data by creating augmented views and training the encoder to produce representations that are invariant to these changes, yielding robust and discriminative features for downstream classification.

Deep learning methods for multivariate time-series classification commonly require large amounts of labeled data, but manual annotation is labor-intensive and impractical at scale. To address this, Contrastive Learning has been adopted as a promising alternative. It constructs multiple augmented views of unlabeled data and trains the model to learn representations that stay invariant under such perturbations. This objective strengthens the encoder against noise and disturbances, enabling it to extract robust and discriminative features. In the time-series setting, most CL approaches concentrate on temporal consistency, using time-domain augmentations such as jittering or permutation to generate views and stabilize temporal patterns. The overarching goal is therefore twofold: mitigate the annotation bottleneck and learn high-quality representations without labels.