Signature features with the visibility transformation
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The signature in rough path theory provides a graduated summary of a path through an examination of the effects of its increments. Inspired by recent developments of signature features in the context of machine learning, we explore a transformation that is able to embed the effect of the absolute position of the data stream into signature features. This unified feature is particularly effective for its simplifying role in allowing the signature feature set to accommodate nonlinear functions of absolute and relative values.
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