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- W3036690558 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, i.e., functions from [0, 1] to Rš, 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 continuous paths. After a survey of basic principles of signatures, we investigate the influence of embeddings on prediction accuracy with an in-depth study of recent and challenging datasets. We show that a specific embedding, called lead-lag, is systematically better, whatever the dataset or algorithm used." @default.
- W3036690558 created "2020-06-25" @default.
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- W3036690558 date "2020-01-01" @default.
- W3036690558 modified "2023-09-25" @default.
- W3036690558 title "Learning with Signatures" @default.
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- W3036690558 doi "https://doi.org/10.1007/978-3-030-47756-1_4" @default.
- W3036690558 hasPublicationYear "2020" @default.
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