Gaussian processes based data augmentation and expected signature for time series classification

• URL: http://arxiv.org/abs/2310.10836v1
• Date: Mon, 16 Oct 2023 21:18:51 GMT
• Title: Gaussian processes based data augmentation and expected signature for time series classification
• Authors: Marco Romito and Francesco Triggiano
• Abstract summary: We propose a feature extraction model for time series built upon the expected signature. One of the main features is that an optimal feature extraction is learnt through the supervised task that uses the model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The signature is a fundamental object that describes paths (that is, continuous functions from an interval to a Euclidean space). Likewise, the expected signature provides a statistical description of the law of stochastic processes. We propose a feature extraction model for time series built upon the expected signature. This is computed through a Gaussian processes based data augmentation. One of the main features is that an optimal feature extraction is learnt through the supervised task that uses the model.

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