This is the public github repository for the workshop that will be given at AMLD 2022 on 27th of March.
Link to WOrkshop page: https://appliedmldays.org/events/amld-epfl-2022/workshops/forecasting-meta-learning
Recently, new deep learning models have emerged producing state-of-the-art results in domains from sales and demand forecasting to energy. However, applying these models in practice can still be a challenge: from formatting the data, to implementing the models or obtaining forecasts that factor in external data.
In this workshop, we will share our learning and techniques to train and apply deep learning forecasting models in practice. We will also talk about meta-learning, which is a new avenue opened by these models. Meta-learning enables the discovery of generic patterns from a diverse set of distinct time series and improve generalization on new time series coming from potentially different datasets, opening the door for zero-shot forecasting and avoiding the cold start problem.
This Github contains a notebook to be completed during the workshop. The complete notebook will be uploaded after the workshop here.
- Workshop slides: https://docs.google.com/presentation/d/1iViBCRa-sG_8KzJrqbyI_Vv08Zi9KmQ73Vkk9KohyKA/edit?usp=sharing
- Darts Documentation https://unit8co.github.io/darts/