To register please click here.

The modules are organized as a Summer Course by the COST Action HiTEc and are chaired by Prof. Erricos Kontoghiorghes and Prof. Ana Colubi on behalf of the Action. The participation of COMPSTAT 2026 attendees is sponsored by IASC. Any other participant must register using the link below.

The Summer Course will consist of three modules plus two keynote talks as described below. Participants are requested to bring their own laptop.

Dates: 25-27 August 2026
Venue: Athens University Law School building, Solonos 57, Athens 106 79, Greece.
Room: TBA.

Trainers:
Module I: Weining Wang, University of Bristol, UK.
Module II: Michael Pitt, King's College London, UK.
Module III: Matus Maciak, Charles University in Prague, Czech Republic.

Keynote talks:
Peter Buehlmann, ETHZ, Switzerland.
Martina Mittlboeck, Medical University of Vienna, Austria.

• In order to apply for the grants, candidates should submit their CV by e-mail to hiteccostaction@gmail.com.
• Deadline for applications: 15th March 2026.
• Granted candidates will be informed by e-mail after the deadline and must send their flight tickets and registration 7 days after the notification to secure their grants. Otherwise, their grants will be revoked and assigned to other candidate.
• The granted candidates must attend all the sessions and sign the attendance list in order to obtain their grants.

• The granted candidates must attend all the sessions of the course in order to obtain their grants.

Time Series Forecasting: From Classical Methods to Transformers and Diffusion Models
Weining Wang, University of Bristol, UK.
Description:

This module provides a concise yet rigorous introduction to modern time series forecasting, bridging classical econometric methods and recent advances in machine learning. We begin with foundational concepts, including ARIMA, highlighting their probabilistic structure and forecasting principles. The course then reformulates forecasting as a supervised learning problem, introducing machine learning approaches such as neural networks.

The second part of the module focuses on attention-based models and Transformers, explaining the self-attention mechanism and its advantages for modeling long-range temporal dependencies. We discuss global forecasting models and cross-series learning in high-dimensional settings. Finally, we introduce diffusion models for probabilistic forecasting, emphasizing their ability to generate full predictive distributions and capture complex, multimodal uncertainty.

Throughout the module, we connect modern methods to econometric theory, discussing issues of estimation and inference. The aim is to equip participants with both practical tools and conceptual understanding of current research frontiers in time series forecasting.

TBA
Michael Pitt, King's College London, UK.
Description: TBA

TBA
Matus Maciak, Charles University in Prague, Czech Republic.
Description: TBA

Tuesday, 25 August 2026

• 09:30 – 10:30 Keynote Talk
• 10:30 – 11:00 Coffee break
• 11:00 – 12:30 Module I (Weining Wang)
• 12:30 – 14:00 Lunch break
• 14:00 – 15:30 Module I (Weining Wang)
• 15:30 – 16:00 Coffee break
• 16:00 – 17:00 Module I (Weining Wang)

Wednesday, 26 August 2026

• 09:00 – 10:30 Module II (Michael Pitt)
• 10:30 – 11:00 Coffee break
• 11:00 – 12:30 Module II (Michael Pitt)
• 12:30 – 14:00 Lunch break
• 14:00 – 16:00 Module II (Michael Pitt)

Thursday, 27 August 2026

• 09:00 – 10:00 Module II (Michael Pitt)
• 10:00 – 10:30 Coffee break
• 10:30 – 12:30 Module III (Matus Maciak)
• 12:30 – 14:00 Lunch break
• 14:00 – 15:00 Keynote Talk
• 15:00 – 15:30 Coffee break
• 15:30 – 17:30 Module III (Matus Maciak)