Convolutional signature for sequential data

Ming Min () and Tomoyuki Ichiba ()
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Ming Min: University of California
Tomoyuki Ichiba: University of California

Digital Finance, 2023, vol. 5, issue 1, No 2, 3-28

Abstract: Abstract Signature is an infinite graded sequence of statistics known to characterize geometric rough paths. While the use of the signature in machine learning is successful in low-dimensional cases, it suffers from the curse of dimensionality in high-dimensional cases, as the number of features in the truncated signature transform grows exponentially fast. With the idea of Convolutional Neural Network, we propose a novel neural network to address this problem. Our model reduces the number of features efficiently in a data-dependent way. Some empirical experiments including high-dimensional financial time series classification and natural language processing are provided to support our convolutional signature model.

Keywords: Signature; Rough paths; Convolutional neural networks; Sequential data; 60L10; 62R07; 68T50; C630 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42521-022-00049-7

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