T-Rep: Representation Learning for Time Series using Time-Embeddings
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Keywords: Multivariate time series, Self-supervised, Time series representations, Temporal features, Time-Embeddings, Representation Learning, Missing data
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TL;DR: T-Rep is a self-supervised method to learn time series representations at a time-step granularity that outperforms existing self-supervised algorithms in classification, forecasting and anomaly detection tasks
Abstract: Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data. To address this, we propose T-Rep, a self-supervised method to learn time series representations at a timestep granularity. T-Rep learns vector embeddings of time alongside its feature extractor, to extract temporal features such as trend, periodicity, or distribution shifts from the signal. These time-embeddings are leveraged in pretext tasks, to incorporate smooth and fine-grained temporal dependencies in the representations, as well as reinforce robustness to missing data. We evaluate T-Rep on downstream classification, forecasting, and anomaly detection tasks. It is compared to existing self-supervised algorithms for time series, which it outperforms in all three tasks. We test T-Rep in missing data regimes, where it proves more resilient than its counterparts. Finally, we provide latent space visualisation experiments, highlighting the interpretability of the learned representations.
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Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Submission Number: 5683
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