IA ANR-DFG-JST - Appel trilatéral ANR-DFG-JST en Intelligence Artificielle (IA)

EnhanceD Data stream Analysis : combining the signature method and machine learning algorithms – EDDA

Submission summary

Multidimensional time series are ubiquituous in many fields of science an industry. Most of the time, linear models are insufficient to capture the complex nature of the data.
Time series analysis from a machine learning point of view is largely driven by the desire to provide interpretable methods using nonlinear features.
The EDDA project intends to improve existing approaches by considering the iterated-integrals signature (IIS) features, which encodes nonlinear information in multivariate time series.
Strong interdisciplinary collaborations are needed to face the challenges of integrating in a relevant manner the IIS feature in machine learning algorithms.
The EDDA project aims at bringing together scientific experts from the disciplines of machine learning, stochastic analysis
and practitionners in oceanography to work together towards the common goals of 1) exploring and exploiting interpretability of the IIS feature, 2) identifying fundamental strategies and methods to elaborate IIS-enhanced machine learning algorithms, and 3) developing relevant applications in data-driven multidimensional time series analysis.

Project coordinator

Madame Marianne Clausel (Institut Elie Cartan de Lorraine)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


IECL Institut Elie Cartan de Lorraine

Help of the ANR 309,960 euros
Beginning and duration of the scientific project: February 2021 - 36 Months

Useful links

Sign up for the latest news:
Subscribe to our newsletter