Biology

Discriminative model

Transfer of learning

Raw data

Feature learning

Similarity (geometry)

Time series

Classifier (UML)

Economics

Set (abstract data type)

Data mining

Computer science

Code (set theory)

Paleontology

Task (project management)

Pattern recognition (psychology)

Politics

Management

Image (mathematics)

Political science

Artificial intelligence

Temporal database

Series (stratigraphy)

Representation (politics)

Programming language

Machine learning

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