Distribution-free prediction intervals for time series forecasting
Trustworthy machine learning (ML) systems should not only predict what they know, but also what they don’t know. However, in real-life applications, it often happens that ML systems are not able to quantify their uncertainty in a correct way. In this talk Prof. Waegeman will discuss various methods to quantify uncertainty in regression and time series forecasting settings. In the first part of the talk, he will present recent benchmarking results for methods that estimate prediction intervals in the classical regression setting with i.i.d. data. In the second part of the talk, he will elaborate on extensions of these methods in the time series forecasting setting. Compared to the i.i.d. setting, estimation of prediction intervals in time series forecasting can be very challenging, especially in the presence of autocorrelation and non-stationarity.
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- Inschrijvingen: tot 10 feb 2022
- Prijs: gratis
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Willem Waegeman
Willem Waegeman is an associate professor at Ghent University, and a member of the research unit Knowledge-based Systems (KERMIT) of the Department of Data Analysis and Mathematical Modelling. His main interests are machine learning and bioinformatics. Specific interests include multi-target prediction problems, uncertainty quantification, sequence models and deep learning.
Willem Waegeman is an author of more than 100 papers of peer-reviewed journals and conferences, and his work has won several prizes. In recent years he has served on the program committees of leading conferences in his field (ICML, NIPS, ECML/PKDD, AAAI, AISTATS, IJCAI, etc.).
Since 2008 he is lecturing a machine learning course in Ghent. Since 2014 he is also lecturing several introductory math courses in the first bachelor (> 500 students per year). Willem Waegeman is currently supervising eight PhD-students.
AI for Time Series Seminars
Verscheidene onderzoeksgroepen in het Vlaams AI-onderzoeksprogramma verrichten onderzoek van wereldklasse in verband met tijdreeksen, zowel voor de ontwikkeling van algoritmes en tools, als voor een brede reeks toepassingen. In een recente rondvraag bij de Vlaamse AI-gemeenschap, bleek dat het onderwerp ’tijdreeksen’ het meest gevraagd werd om toekomstige workshops en cursussen over te organiseren. Met deze seminariereeks komen we aan die vraag tegemoet en brengen we onderzoekers die geïnteresseerd zijn in, of onderzoek verrichten naar, tijdreeksen samen. We bieden hen en andere belangstellenden een gevarieerd programma met nationale en internationale sprekers.
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