Publication
MISTI: Multi-Style Transfer for Multivariate Time Series
Henri Hoyez; Bruno Mirbach; Cedric Schockaert; Jason Raphael Rambach; Didier Stricker
In: IEEE (Hrsg.). 33rd. European Signal Processing Conference (EUSIPCO-2025), September 8-12, Palermo, Italy, IEEE, 2025.
Abstract
Time series data are generally easy to obtain but
often suffer from issues such as incomplete labeling, missing
values, and privacy constraints. Transferring data from one
domain to another using Machine Learning offers a promising
solution to these challenges. This paper introduces a novel
feed-forward multi-style transfer algorithm for time series. The
proposed approach utilizes dual encoders to disentangle content
and style from input sequences, which are then recombined by a
decoder to generate sequences with the specified characteristics.
Additionally, we propose a new metric to evaluate domain
shifts and quantify implicit differences between datasets. Our
method demonstrates robust transfer performance across diverse
datasets, ranging from synthetic datasets to multivariate human
activity recognition time series.