KOMPOS: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines

Author:

Koutroulis Georgios1,Botler Leo2,Mutlu Belgin1,Diwold Konrad3,Römer Kay2,Kern Roman2

Affiliation:

1. Pro2Future GmbH, Inffeldgasse, Graz, Austria

2. Graz University of Technology, Graz, Austria

3. Pro2Future GmbH and Graz University of Technology, Graz, Austria

Abstract

Recovering causality from copious time series data beyond mere correlations has been an important contributing factor in numerous scientific fields. Most existing works assume linearity in the data that may not comply with many real-world scenarios. Moreover, it is usually not sufficient to solely infer the causal relationships. Identifying the correct time delay of cause-effect is extremely vital for further insight and effective policies in inter-disciplinary domains. To bridge this gap, we propose KOMPOS, a novel algorithmic framework that combines a powerful concept from causal discovery of additive noise models with graphical ones. We primarily build our structural causal model from multivariate adaptive regression splines with inherent additive local nonlinearities, which render the underlying causal structure more easily identifiable. In contrast to other methods, our approach is not restricted to Gaussian or non-Gaussian noise due to the non-parametric attribute of the regression method. We conduct extensive experiments on both synthetic and real-world datasets, demonstrating the superiority of the proposed algorithm over existing causal discovery methods, especially for the challenging cases of autocorrelated and non-stationary time series.

Funder

FFG

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

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