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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- Register until: 10 Feb 2022
- Price: free
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Teacher / speaker
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
Several research groups in the Flanders AI Research Program conduct world-class research on time series, both in the development of algorithms and tools, as in a wide area of application fields. In a recent poll in the Flanders AI community, ‘time series’ came up as the most wanted topic for future workshops or courses. With this seminar series, we bring together researchers that are interested in, or are conducting research related to, time series. We offer a varied program of national and international speakers.
Klik on the separate webinars below to (re)watch the recordings.
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