Posts

Visiting Researcher at Meta

Since about a week I’m on a sabbatical for 6 months in the Infrastructure Data Science and Engineering team at Meta Platforms Inc. in Menlo Park, California. I’m fortunate that Zeynep Erkin Baz, Dario Benavides, and Ashish Kelkar have given me this opportunity to work with them in their great team. I’m looking forward to tackling interesting forecasting problems and learning a lot more about forecasting and other things along the way.

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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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Our new time series repository: Forecastingdata.org

We have put together a new time series data repository for forecasting. It is dedicated to sets of series for cross-learning/global modelling, and to single but very long series (the longest ones over 7 million data points). We were also not too happy with any existing data format, so we developed a new one and we think it is quite useful and versatile.

Paper link: https://arxiv.org/abs/2105.06643

Website: https://forecastingdata.org

IEEE-CIS Competition

Congratulations to my PhD students Kasun, Hansika, and Rakshitha for their 4th place in the IEEE-CIS Technical Challenge on Energy Prediction from Smart Meter Data

Well done! Kasun describes some of the methodology here

Also congratulations to Alex Dokumentov for his 6th place (3rd in terms of accuracy).

Neural Prophet released

We have released the Neural Prophet software, a reimplementation of “prophet” in PyTorch. It is joint work with Facebook and Stanford University.

Announcements from Facebook are here and here

The code is here

Tutorial at ACML2020 on Forecasting for Data Scientists

I’m giving a 2.5 hour tutorial at ACML2020 on Forecasting for Data Scientists, covering all about forecasting that Data Scientists and Machine Learners should know.

Watch the recording here

Get additional information here

Special session at Virtual ISF2020 on Global Modelling for Forecasting

Pablo Montero-Manso and I are organising an invited session at Virtual ISF2020 on Global Modelling for Forecasting. Details are here Speakers and topics are: Kasun Bandara (Speaker) Student, Monash University Transfer Learning Schemes for Global Forecasting Models using Recurrent Neural Networks Alexey Chernikov (Speaker) Student, Monash Automatic Feature-based Forecast Model Averaging Dilini Rajapaksha (Speaker) PhD Student, Monash University Local Model-Agnostic Interpretability in Global Time Series Forecasting

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Congratulations to my PhD students for their M5 success

A team of 4 Monash PhD students, three of whom I supervise (Kasun Bandara, Rakshitha Godahewa and Hansika Hewamalage) have achieved a 17th place in the M5 forecasting competition, with over 5000 participants. Congratulations!