CounterfacTS

Tool for Probing the Robustness of Deep Learning Time-series Forecasting Models

This is the code from our paper Kjærnli et al. 2024: Probing the Robustness of Time-series Forecasting Models with CounterfacTS

Running the application

To run the application start by creating and activating the environment after cloning the repository in your machine:

conda env create -f env.yaml
conda activate counterfacts

We can then use the following command to run the application

bokeh serve src/ --args <config-path>

where the config_path is the path to a config.yaml file in the experiments folder. As a concrete example, this command will run the application using a simple dense network on the electricity dataset:

bokeh serve src/ --args experiments/electricity_nips/feedforward/config.yaml

Finally, copy the http address that appears in the terminal and paste it in the browser to open the interactive GUI.