Work

FRUITS: feature extraction using iterated sums for time series classification

Year: 2024

Type: article

Abstract: We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, under certain settings, are invariant to a form of time-warping. We achieve competitive results, both in accuracy and speed, on the UCR archive. We make our code available at https://github.com/irkri/fruits .

Source: Data Mining and Knowledge Discovery

DOI: 10.1007/s10618-024-01068-1

Authors: Joscha Diehl, Richard Krieg

Institution: Universität Greifswald

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,090

Funders: Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Universität Greifswald