Visualization of XAI Interpretations for Time Series Prediction

When applying models, it is necessary to make individual decisions and the model interpretable and explainable. White-box models such as linear regression are straightforward to interpret and explain but are limited in their performance, especially if the model’s data generating process differs. Therefore, empirical, data-based machine learning models are often used because they require little to no expert knowledge of the data when modeling. However, many of these are grey or black-box models, hence very complex and performant models that cannot (or only sparsely) be explained by the model and do not allow for statistical inference. Their results are not comprehensible, and model behavior cannot be explained.

In Explainable AI (XAI), a subfield of Artificial Intelligence, methods are developed to make such models comprehensible and interpret individual decisions. In addition to the development of XAI methods, suitable visualizations of interpretations need to be developed, for example, for complex time series data, e.g., multivariate time series.

The goal of this thesis is to find suitable visualizations of interpretations for different XAI methods for (multivariate) time series forecasts. The aim is to develop visualizations that allow the comparability of interpretations across different forecast models or XAI methods.

This is a bachelor thesis topic.


  • Experience with time series analysis and prediction, good comprehension of mathematical/statistical basics are helpful
  • Experience with visualization of time series is desirable
  • Very good comprehension of Machine Learning, first experiences with XAI methods are advantageous
  • Good programming skills (ideally Python)


  • Guidotti, Riccardo & Monreale, Anna & Turini, Franco & Pedreschi, Dino & Giannotti, Fosca. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys. 51. 10.1145/3236009.
  • W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders and K. -R. Müller, “Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,” in Proceedings of the IEEE, vol. 109, no. 3, pp. 247-278, March 2021, doi: 10.1109/JPROC.2021.3060483.
  • Schlegel, Udo & Arnout, Hiba & El-Assady, Mennatallah & Oelke, Daniela & Keim, Daniel. (2019). Towards A Rigorous Evaluation Of XAI Methods On Time Series. 4197-4201. 10.1109/ICCVW.2019.00516.