  A common issue for machine learning models applied to time-series forecasting
is the temporal evolution of the data distributions (i.e., concept drift).
Because most of the training data does not reflect such changes, the models
present poor performance on the new out-of-distribution scenarios and,
therefore, the impact of such events cannot be reliably anticipated ahead of
time. We present and publicly release CounterfacTS, a tool to probe the
robustness of deep learning models in time-series forecasting tasks via
counterfactuals. CounterfacTS has a user-friendly interface that allows the
user to visualize, compare and quantify time series data and their forecasts,
for a number of datasets and deep learning models. Furthermore, the user can
apply various transformations to the time series and explore the resulting
changes in the forecasts in an interpretable manner. Through example cases, we
illustrate how CounterfacTS can be used to i) identify the main features
characterizing and differentiating sets of time series, ii) assess how the
model performance depends on these characateristics, and iii) guide
transformations of the original time series to create counterfactuals with
desired properties for training and increasing the forecasting performance in
new regions of the data distribution. We discuss the importance of visualizing
and considering the location of the data in a projected feature space to
transform time-series and create effective counterfactuals for training the
models. Overall, CounterfacTS aids at creating counterfactuals to efficiently
explore the impact of hypothetical scenarios not covered by the original data
in time-series forecasting tasks.
