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


Recent publications

  • 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
  • Seyedali Meghdadi, Guido Tack, Ariel Liebman, Nicolas Langrene, Christoph Bergmeir (2021) Versatile and Robust Transient Stability Assessment via Instance Transfer Learning. In: Working Paper. Abstract  pdf bib
  • Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I Webb (2021) Time series extrinsic regression. In: Data Mining and Knowledge Discovery, 35, (3), pp. 1032-1060. Abstract  pdf bib
  • Dilini Rajapaksha, Chakkrit Tantithamthavorn, Christoph Bergmeir, Wray Buntine, Jirayus Jiarpakdee, John Grundy (2021) SQAPlanner: Generating data-informed software quality improvement plans. In: IEEE Transactions on Software Engineering. Abstract  pdf bib
  • Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara (2021) Recurrent neural networks for time series forecasting: Current status and future directions. In: International Journal of Forecasting, 37, (1), pp. 388-427. Abstract  pdf 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...