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 30. 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 to Meet Demand
Instructor: Stephan Kolassa, SAP Switzerland AG
9am – 12pm

Demand is influenced by various drivers, from the “standard” ones treated in every forecasting course and textbook like seasonality, trend etc. to causal factors we can influence, like prices or promotions, to factors we cannot influence, like the weather or a competitors’ marketing activities. We will discuss forecasting as one ingredient into other processes, data and data quality (with a particular emphasis on causal drivers), the forecasting process itself and forecast quality measurement.

Workshop 2: Sky is the Limit: Bringing the Exponential Smoothing to the Next Level
Instructors: Ivan Svetunkov and Kandrika Pritularga, Lancaster University
9am – 12pm

Exponential smoothing is one of the most popular forecasting approaches used in practice. It is robust, it has performed very well in many forecasting competitions and is easy to implement and interpret. While the exponential smoothing in the ETS form works well in many contexts, there have been many advances in forecasting area making it applicable in a wide variety of areas. These include ETS with explanatory variables (ETSX), intermittent demand ETS, high frequency modifications of the model, different techniques for the model estimation, selection and combination of forecasts and more.

Workshop 3: Machine Learning
Instructors: Tim Januschowski, Zalando; Lorenzo Stella, Amazon; Kashif Rasul, Morgen Stanley Research
1 – 4pm

In this in-person workshop, we aim at covering neural forecasting methods from 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. The workshop 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.

Workshop 4: Artificial Intelligence and Neural Networks in Forecasting
Instructor: Dr. Hans Georg Zimmerman, Chief Scientist at Fraunhofer IIS
9am – 4pm

Artificial Intelligence is an integrated framework of perception, modelling and action planning. In this tutorial we will focus perception on multi-dimensional time series, work out the mathematics of neural networks as a modelling framework and finally ask how to use such models in action planning. A focus on decision support has to imply risk analysis as an important challenge. The tutorial is not only a standalone introduction of mathematical techniques, but it trains a thinking style which balances the handling of data and first principles. [click above link for a full description]

Workshop 5: Business Forecasting: Techniques, Application and Best Practices
Instructors: Eric Stellwagen and Sarah Darin, Business Forecast Systems
9am – 4pm

This workshop surveys commonly implemented business forecasting methods, explains how they work conceptually, reveals their strengths and limitations, and offers best practices for applying them in a business environment. Numerous real-life examples from a range of industries will be presented. The workshop will utilize the Forecast Pro software to illustrate how the techniques are applied to corporate data.

You will leave the workshop with a working knowledge of quantitative and qualitative forecasting methods, enabling you to improve your forecast process and your forecast accuracy.