Articles | Volume 19, issue 6
https://doi.org/10.5194/gmd-19-2385-2026
https://doi.org/10.5194/gmd-19-2385-2026
Development and technical paper
 | 
25 Mar 2026
Development and technical paper |  | 25 Mar 2026

The spatio-temporal visualization tool HMMLVis in renewable energy applications

Rainer Wöß, Kateřina Hlaváčková-Schindler, Irene Schicker, Petrina Papazek, and Claudia Plant

Model code and software

Code and data for: The Spatio-Temporal Visualization Tool HMMLVis in Renewable Energy Applications Rainer Wöß https://doi.org/10.5281/zenodo.13885371

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Short summary
Our tool is an easy-to-use, interpretable causal inference software. It can be applied in any scientific discipline exploring time series. The tool uses heterogeneous Granger causality. It can be used on time-series data to infer causal relationships between multiple variables and a target time-series. The tool is demonstrated on different types of applications related to meteorological events in a renewable energy, air pollution, and postprocessing benchmark data.
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