Time-Series Representation Learning via Temporal and Contextual Contrasting (2106.14112v1)

Abstract: Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data. First, the raw time-series data are transformed into two different yet correlated views by using weak and strong augmentations. Second, we propose a novel temporal contrasting module to learn robust temporal representations by designing a tough cross-view prediction task. Last, to further learn discriminative representations, we propose a contextual contrasting module built upon the contexts from the temporal contrasting module. It attempts to maximize the similarity among different contexts of the same sample while minimizing similarity among contexts of different samples. Experiments have been carried out on three real-world time-series datasets. The results manifest that training a linear classifier on top of the features learned by our proposed TS-TCC performs comparably with the supervised training. Additionally, our proposed TS-TCC shows high efficiency in few-labeled data and transfer learning scenarios. The code is publicly available at https://github.com/emadeldeen24/TS-TCC.

Time-Series Representation Learning via Temporal and Contextual Contrasting: A Detailed Analysis

The paper introduces an unsupervised framework for time-series representation learning, termed Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC). The authors address the complexity inherent in learning from unlabeled time-series data, particularly the challenge posed by the temporal dynamics and the lack of labeled data.

Methodological Advancements

The TS-TCC framework innovatively combines self-supervised learning techniques with contrastive modules to extract meaningful representations from time-series data. The approach is structured around two primary modules: temporal contrasting and contextual contrasting.

1. Temporal Contrasting Module: This component focuses on harnessing temporal features by implementing a cross-view prediction task. Two sets of augmentations—strong and weak—are applied to the raw data to generate distinct yet correlated views. The temporal contrasting module attempts to predict future timesteps of one augmentation using the past of the other, thus forcing the model to capture more robust temporal relationships and dependencies.
2. Contextual Contrasting Module: Built on top of the temporal module, this component aims to enhance the discriminative power of the representations. It maximizes the similarity between different context vectors of the same sample while minimizing the similarity with those of different samples. This approach promotes the learning of invariant and discriminative features.

Experimental Results

The efficacy of TS-TCC is demonstrated across three real-world datasets related to human activity recognition, sleep stage classification, and epileptic seizure prediction. The results are compelling:

• TS-TCC achieved performance close to that of supervised learning models when employing a linear classifier on the learned representations.
• The model exhibits robustness and effectiveness in scenarios with few labeled data, significantly outperforming supervised approaches in low-label environments.

Additionally, the experiments also tested the transfer learning capabilities of the framework, showing improved adaptability and transferability of the learned features across different domains, as evidenced by the fault diagnosis dataset experiments.

Implications and Future Directions

The TS-TCC framework addresses the limitations of prior contrastive learning methods that often failed to maintain temporal dependencies in time-series data. By introducing cross-view consistency and employing suitable time-series augmentations, this work advances the field of time-series analysis by proposing a method that integrates temporal forecasting with contextual understanding.

Potential future work could explore the applicability of the framework to other domains where temporal dependencies are pivotal, such as finance or geospatial analysis. Additionally, further refinement of augmentation strategies could enhance model performance across more diverse datasets.

Conclusion

This paper provides a comprehensive framework for unsupervised time-series representation learning, leveraging temporal and contextual contrasting. Through rigorous empirical evaluation and methodical design, the authors contribute a flexible and efficient tool for extracting robust features from time-series data, highlighting significant advancements in self-supervised learning for sequential data.