David Obst

David Obst
Électricité de France (EDF) | EDF

About

6
Publications
1,929
Reads
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103
Citations
Additional affiliations
January 2019 - present
École Nationale de la Statistique et de l'Administration Économique
Position
  • Research Assistant
Description
  • Introduction à la Statistique
January 2019 - present
ENSTA ParisTech
Position
  • Research Assistant
Description
  • Optimisation Quadratique
December 2018 - present
Électricité de France (EDF)
Position
  • PhD Student
Description
  • Textual Data for Time Series Forecasting

Publications

Publications (6)
Article
Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. While several studies address the problem of what to transfer, the very important question of when to answer remains mostly unanswered, especially from a theoretical point-of-view for regression problems. A new theoretica...
Article
The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calenda...
Preprint
Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. While many empirical studies illustrate the benefits of transfer learning, few theoretical results are established especially for regression problems. In this paper a theoretical framework for the problem of parameter tra...
Preprint
The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calenda...
Article
Forecasting wind power generation up to a few hours ahead is of the utmost importance for the efficient operation of power systems and for participation in electricity markets. Recent statistical learning approaches exploit spatiotemporal dependence patterns among neighbouring sites, but their requirement of sharing confidential data with third par...
Preprint
Full-text available
While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to predict time series of national...

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