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Embedding and learning with signatures

Abstract : Sequential and temporal data arise in many fields of research, such as quantitative finance, medicine, or computer vision. The present article is concerned with a novel approach for sequential learning, called the signature method, and rooted in rough path theory. Its basic principle is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This approach relies critically on an embedding principle, which consists in representing discretely sampled data as paths, i.e., functions from [0,1] to R^d. After a survey of machine learning methodologies for signatures, we investigate the influence of embeddings on prediction accuracy with an in-depth study of three recent and challenging datasets. We show that a specific embedding, called lead-lag, is systematically better, whatever the dataset or algorithm used. Moreover, we emphasize through an empirical study that computing signatures over the whole path domain does not lead to a loss of local information. We conclude that, with a good embedding, the signature combined with a simple algorithm achieves results competitive with state-of-the-art, domain-specific approaches.
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Contributor : Adeline Fermanian <>
Submitted on : Friday, November 29, 2019 - 4:26:29 PM
Last modification on : Friday, April 10, 2020 - 5:13:35 PM


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  • HAL Id : hal-02387258, version 1


Adeline Fermanian. Embedding and learning with signatures. 2019. ⟨hal-02387258⟩



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