Ramón Christen

PhD Student in Computer Science

Research interests

Originating from signal theory in electrical engineering, my interests are particularly focused on time series analysis. In this realm, my reseach targets on the value of exogenous variables and its integration in modelling. Exogenous variables typically contain information about the target variable with varying influence factors over time. Thus, the integration in modelling requires attention on temporal relevance estimation of exogenous information and how temporal information can be considered with fuzzy methods. In particular, the research aims to enhance time series modelling with temporal information to enhance forecast accuracy.

In this respect, I am interested in:

  • Attention Mechanisms
  • Data Science
  • Exogenous Variables
  • Machine Learning Methods
  • Statistics and Fuzzy Methods
  • Temporal Saliency Detection

Teaching

Current lectures and modules

  • Applied Machine Learning and Predictive Modelling 1, HSLU
  • Big Data Lab Clusters, HSLU
  • Business Intelligence, HSLU
  • Business Intelligence and Decision Support, HSLU
  • Coaching - Thesis, HSLU
  • Data Collection, Integration and Preprocessing, HSLU
  • Python for Data Scientists, HSLU

Projects

Ongoing

  • Research: Extending the Fuzzy Conversational Character Computing (FCCC) framework with additional chracter traits from spoken words.
  • Research in collaboration with University São Paulo (USP): Investigating morphing mechanisms for transformer based time series modelling approaches and enhancing transformer models with morphing exogenous variabls to enhance forecast accuracy.
  • Doctoral Research in Computer Science: «Exogenous Data in Forecasting: Computational Intelligence in Relevance Analysis»

Completed

Publications

Bagemihl, J., Wilke, G., Christen, R., Layec, V., Wache, H., West, M., Ulli-Beer, S., Zapata, J., Stadler, T., Stabauer, F., Laager, D., & Breit, J. (2019). Power Alliance – Extending Power Grid Capacity on the Medium Voltage Level by Incentivizing a New Class of Emerging Flexible Loads. 11.
Christen, R. (2019). Extending Power Grid Capacity on the Medium voltage Level by Incentivizing a New Class of Emerging Flexible Loads.
Christen, R. (2021). Applying Exogenous Data in Load Forecast and the Complexity of Value Estimation.
Christen, R., Layec, V., Wilke, G., & Wache, H. (n.d.). Power Grid Capacity Extension with Conditional Loads instead of Physical Expansions (p. 24).
Christen, R., Layec, V., Wilke, G., & Wache, H. (2019). Technical Validation of the RLS Smart Grid Approach to Increase Power Grid Capacity without Physical Grid Expansion. Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems, 123–130. https://doi.org/10.5220/0007717101230130
Christen, R., Mazzola, L., Denzler, A., & Portmann, E. (2020). Exogenous Data for Load Forecasting: A Review. Proceedings of the 12th International Joint Conference on Computational Intelligence, 489–500. https://doi.org/10.5220/0010213204890500
Christen, R., Mazzola, L., Denzler, A., & Portmann, E. (2022). Distance Metrics for Evaluating the Use of Exogenous Data in Load Forecasting. In D. Ciucci, I. Couso, J. Medina, D. Ślęzak, D. Petturiti, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 469–482). Springer International Publishing. https://doi.org/10.1007/978-3-031-08974-9
Christen, R., Mazzola, L., Denzler, A., & Portmann, E. (2023). Exogenous Data in Forecasting: FARM -- A New Measure for Relevance Evaluation (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2304.11028
Hundertmark, S., Christen, R., & Portmann, E. (2025). Chatbots with Character—An Implementation of Fuzzy Conversational Character Computing. In M. Baczyński, B. De Baets, M. Holčapek, V. Kreinovich, & J. Medina (Eds.), Advances in Fuzzy Logic and Technology (Vol. 15883, pp. 39–53). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-97225-6_4
Majeed, T., Christen, R., Handschuh, M., & Meier, R. (2021). Pattern Dependent Optimized Mowing of Football Fields with an Autonomous Robot. 8.
Mazzola, L., Denzler, A., & Christen, R. (2020). Towards a Peer-to-Peer Energy Market: An Overview. arXiv:2003.07940 [Physics]. https://doi.org/10.48550/arXiv.2003.07940