The ISF offers unique, tailored workshops for symposium registrants. These workshops offer the opportunity to participate in an in-depth look at a specific forecasting theme. Workshops take place on Sunday, June 29. When registering for the symposium, you will be given the option to select workshop(s). The workshop fees are US$75 (1/2 day) or $150 (full day).

If you have already registered and would like to add a workshop, go to the registration page. Once here, simply enter your personal information, and under the registration options, select ‘Already registered? Select this option to add workshop‘

Workshop 1: Forecasting with Temporal Hierarchies
Instructor: Nikolaos Kourentzes, University of Skövde
9am – 12pm

Forecasting with Temporal Hierarchies (THieF) is a relatively new method in time series forecasting. Initially we introduced it as a tool to improve forecast accuracy, irrespective of the selected forecasting models. Nonetheless, THieF goes beyond due to its connections (i) with hierarchical forecasting from a technical perspective, and (ii) with decision making from an organizational perspective, as it connects short- and long-term projections and plans.

Workshop 2: Large Time Series Models: Where we are and where we are going
Instructor: Haixu Wu and Yong Liu, Tsinghua University; Shiyu Wang, ByteDance
9am – 12pm

In the rapidly evolving field of time series forecasting, large time series models (LTMs) have emerged as a pivotal area of research and application. Unlike the canonical specific time series models, LTSM attempts to establish a general model, which can be fast adapted or zero-shot to new tasks or scenarios, unlocking the power of deep models in practical applications.

Workshop 3: Exploratory time series analysis
Instructor: Mitchell O’Hara-Wild, Monash University
1pm – 4pm

Understanding how data changes over time is essential for specifying suitable forecasting models. This practical workshop shares novel solutions to the challenges of exploring large collections of time series and visualizing sub-daily patterns. Discover how to compare patterns across many related series with feature-based graphics, and use a grammar of time series graphics to reveal holiday effects and complex seasonalities in high-frequency data.

Workshop 4: Deep Learning & Foundation Models for Forecasting
Instructors: Tim Januschowski, Databricks; Kashif Rasul, Morgen Stanley Research
1pm – 4pm

In this in-person workshop, we aim to cover neural forecasting methods from the ground up, starting from the very basics of deep learning to well-established deep forecasting model such as DeepAR (Salinas et al., 2019) to more recent forecasting models, including foundation models for which we’ll review selected models.  The workshop will be in-person, with a mix of theoretical lectures and practical sessions. In the lectures, we will focus on the fundamentals of deep learning such as the various architecture types (e.g. feed-forward, convolutional, recurrent neural networks and transformers) and the most important breakthroughs that established the strength of neural networks.