Publications

• Deforce, B., Lee, M.-C., Baesens, B., Asensio, E. S., Yoo, J., & Akoglu, L. (2024). “End-To-End Self-tuning Self-supervised Time Series Anomaly Detection.” arXiv preprint arXiv:2404.02865.

• Deforce, B., Baesens, B., Diels, J., & Asensio, E. S. (2024). “Harnessing the power of transformers and data fusion in smart irrigation.” Applied Soft Computing, Elsevier, 152, 111246.

• Deforce, B., Baesens, B., Diels, J., & Asensio, E. S. (2023). “MultiMix TFT: A Multi-task Mixed-Frequency Framework with Temporal Fusion Transformers.” Conference on Lifelong Learning Agents, PMLR, 586-600.

• Deforce, B., Baesens, B., Diels, J., & Serral Asensio, E. (2022). “Forecasting Sensor-data in Smart Agriculture with Temporal Fusion Transformers.” Transactions on Computational Science & Computational Intelligence. Springer Nature.

• Deforce, B., Baesens, B., Diels, J., & Serral Asensio, E. (2022). “Self-Supervised Anomaly Detection for Detecting Rogue Sensors in IoT Data.” Presented at the European Conference on Operational Research (EURO 2022), Helsinki (Finland).

• Deforce, B., Baesens, B., Diels, J., & Serral Asensio, E. (2022). “Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors.” Presented at the 26th pacific-asia conference on knowledge discovery and data mining, Chengdu, China.