My PhD student Dilini Sewwandi Rajapaksha and I made first place in the FUZZ-IEEE Explainable Energy Prediction Competition. The winners were announced at the IEEE International Conference on Fuzzy Systems, Luxembourg, 2021.
We proposed a novel algorithm to provide Local Interpretable Model-agnostic Rule-based Explanations for Forecasting, a paper and more descriptions will be available hopefully soon.
We’ve just launched the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, with US$20k in prize money. You’ll need to forecast energy demand and solar power production for a couple of buildings on the Monash Clayton campus, and then use the forecasts to optimally schedule lectures and battery charging/discharging. We’re looking forward to your great submissions!
More information is here
- Md Mohaimenuzzaman, Christoph Bergmeir, Ian West, Bernd Meyer (2023) Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices. In: Pattern Recognition, 133, pp. 109025. Abstract bib
- Gautier Pialla, Hassan Ismail Fawaz, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Christoph Bergmeir, Daniel Schmidt, Geoffrey Webb, Germain Forestier (2022) Smooth Perturbations for Time Series Adversarial Attacks. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 485-496. Abstract bib
- Aidan Quinn, Melanie Simmons, Benjamin Spivak, Christoph Bergmeir (2022) RNN-BOF: A Multivariate Global Recurrent Neural Network for Binary Outcome Forecasting of Inpatient Aggression. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. Abstract bib
- Md Mohaimenuzzaman, Christoph Bergmeir, Bernd Meyer (2022) Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-Devices. In: IEEE Access, 10, pp. 6696-6707. Abstract DOI bib
- Priscila Grecov, Ankitha Nandipura Prasanna, Klaus Ackermann, Sam Campbell, Debbie Scott, Dan I Lubman, Christoph Bergmeir (2022) Probabilistic causal effect estimation with global neural network forecasting models. In: IEEE Transactions on Neural Networks and Learning Systems. Abstract bib