arXiv:2605.21241v1 Announce Type: new Abstract: Self-supervised learning for time-series representation aims to reduce reliance on labeled data while maintaining strong downstream performance, yet many existing approaches incur high computational costs or rely on assumptions that do not hold across diverse temporal dynamics. In this work, we introduce Divide and Contrast (Di-COT), an unsupervised framework that avoids data augmentation and multiple encoder passes by contrasting informative substructures within a window rather than individual timesteps. Di-COT stochastically partitions each win
The increasing computational demands and limitations of existing self-supervised learning methods for time-series data necessitate new, more efficient approaches like Di-COT.
This development proposes a more computationally efficient and robust method for learning temporal features without extensive data augmentation, which could accelerate AI development across various time-series applications.
The reliance on data augmentation and multiple encoder passes for self-supervised time-series learning could decrease, leading to faster model development and deployment.
- · AI researchers
- · Time-series data analysts
- · Companies using predictive analytics
- · Hardware manufacturers (indirectly through efficiency gains)
- · Inefficient time-series models
- · Augmentation-heavy ML pipelines
More accurate and faster self-supervised learning for time-series data without augmentation.
Reduced computational costs and energy consumption for developing AI models on temporal data.
This could lead to more energy-efficient AI models, indirectly impacting the energy-bottleneck narrative by reducing power demands for certain AI development.
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Read at arXiv cs.LG