FUZZ-IEEE Explainable Energy Prediction Competition

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.

IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling

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

Special session at Virtual ISF2021 on Recent Advances in Global Modelling for Forecasting

We’ve been organising an invited session at Virtual ISF2021 on Recent Advances in Global Modelling for Forecasting. Details on part 1 and 2 are here and here Session 1: Time series feature embedding for forecasting with deep learning Speaker: James Nguyen A Look at the Evaluation Setup of the M5 Forecasting Competition Speaker: Hansika Hewamalage Dependency Learning Graph Neural Networks for Multivariate Forecasting Speaker: Abishek Sriramulu

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Recent publications

  • 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
  • Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I. Webb (2022) MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification. In: Data Mining and Knowledge Discovery, (forthcoming). Abstract  pdf bib
  • Dilini Rajapaksha, Christoph Bergmeir (2022) LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Eletricity Smart Meter Data. In: Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). Abstract bib
  • Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Christoph Bergmeir, Ricardo J. Bessa, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Piotr Fiszeder, Philip Hans Franses, Michael Gilliland, M. Sinan Gonul, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Andrew B. Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Onkal, Alessia Paccagnini, Ioannis Panapakidis, Jose M. Pavia, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patricia Ramos, David E. Rapach, J. James Reade, Bahman Rostami-Tabar, Michal Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramon Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova, Florian Ziel (2022) Forecasting: theory and practice. In: International Journal of Forecasting, (forthcoming). Abstract  pdf bib
  • Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I Webb, Pablo Montero-Manso (2022) An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting. In: International Journal of Forecasting, (forthcoming). Abstract bib

Recent and upcoming talks

  • ACML 2020 Tutorial: Forecasting for Data Scientists. (18 November 2020) More info...
  • Facebook Forecasting Summit: Forecasting for Data Scientists. (6 October 2020) More info...
  • Recurrent Neural Networks for Forecasting. (21 November 2019) More info...