Laurent Oudre

In many application contexts (health, industry, climatology…), the data collected take the form of time series. The fundamental challenge then consists in choosing a suitable representation, allowing the temporal information to be taken into account as well as possible.

Machine Learning for time series encompasses a large number of unsupervised or supervised tasks such as prediction, classification, completion/interpolation, clustering, segmentation/change-point detection or anomaly detection. But in reality, most of the work for a data scientist dealing with temporal data consists of a series of hidden tasks:

• Understand the data: know where they come from, how they were acquired, what their characteristics are, interact with domain experts
• Improve the data: find accurate representation spaces, consolidate the data (denoising, detrending, outlier removal)
• Model the data: physical/statistical or expert-based models, simple, adaptive and interpretable models
• Extract information from the data: find repetitive patterns, features of interest, change-points

This course aims to provide an overview of ML techniques to study time series, mostly focused on these often poorly documented hidden tasks, widely illustrated with real data and usecases. Note that in its current form, the course will only marginally discuss Deep Learning algorithms.

Lectures take place on Monday mornings at ENS Paris-Saclay. They are held on-site only, and are neither filmed nor recorded. Lectures are given in French, while all course material is in English. Tutorial sessions, led by Valerio Guerrini, are held either on Monday mornings on-site or on Monday afternoons on Zoom. The content is identical in both slots, so students should attend only one. Attendance at the lectures is mandatory. Please note that auditeurs libres cannot attend due to the large number of students.

• Tutorials (25%): commented notebooks and/or PDF reports. Attendance at at least one session per tutorial (on-site or remote) is mandatory. Missed or late assignments → FAIL.
• Mini-project (75%): one paper on a topic related to the course, done in pairs.
  • Report (25%): PDF, 5 pages, template provided
  • Source code (25%): commented Jupyter notebook
  • Oral presentation (25%): 10 min with slides

Report and source code must be submitted by 13 December 2026 (23:59) or 3 January 2027 (23:59), depending on the date of the oral presentation.

Registration form distributed at the first lecture. Final registration deadline: 8 October 2026.