​The introduction of complex ML and DL methods for financial time series forecasts potentially enables higher predictive accuracy but this comes at the cost of higher complexity and thus lower interpretability. For cross sectional data classical XAI approaches can lead to valuable insights about the models’ inner workings, but these techniques generally cannot cope well with longitudinal data (time series) in the presence of dependence structure and non-stationarity. The literature currently does not offer any XAI approach that is specifically developed for financial time series. Further research is needed on developing explainability methods that can be applied to complex models like deep learning methods (DL) which preserve and exploit the natural time ordering of the data.