Past events

The list of events I have delivered in the past:

Demand Forecasting with the Augmented Dynamic Adaptive Model
  • Date & time 29th Apr 2024, at 10:00 UK time
  • Format: Online
  • Link to the event
  • Organiser: Forecasting for Social Good (F4SG)
  • Description: Files for the workshop. Abstract: Ivan Svetunkov will deliver an online workshop with examples in R explaining how to use ADAM in several scenarios. The workshop will be delivered in a format of short interactive lectures supported by examples in R based on Ivan’s monograph “Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)“. […] Read More
Why you should care about exponential smoothing Why you should care about exponential smoothing
  • Date & time 1st May 2024, at 13:00 UK time
  • Format: Blended
  • Link to the event
  • Organiser: Management Science Department of Lancaster University
  • Description: Abstract: In the age of Machine Learning, many data scientists have started using more complicated approaches for forecasting, such as GAM, XGBoost, k-NN, Artificial Neural Networks etc. Due to the increased interest in these methods, the simpler and robust approaches might look less attractive than the more innovative ones and as a result tend to […] Read More
Why you should care about exponential smoothing Why you should care about exponential smoothing: demand planners perspective
  • Date & time 10th May 2024, at 14:00 UK time
  • Format: Online
  • Organiser: Haleon
  • Description: Abstract: In the age of Machine Learning, many data scientists have started using more complicated approaches for forecasting, such as GAM, XGBoost, k-NN, Artificial Neural Networks etc. Due to the increased interest in these methods, the simpler and robust approaches might look less attractive than the more innovative ones and as a result tend to […] Read More
iETS: State space model for intermittent demand forecasting
  • Date & time 14th May 2024, at 17:00 UK time
  • Format: Online
  • Link to the event
  • Organiser: Time Series Analysis And Forecasting Society (TAFS)
  • Description: Abstract: Making decisions about inventory for items with intermittent demand poses a significant challenge. When deciding to discontinue sales of a product, decisions often hinge on average demand estimates, while replenishment decisions rely on intervals of demand estimates rather than single points. Addressing the modeling of intermittent demand becomes crucial in this context. Previous studies […] Read More
Methods, Models: a Modern Perspective (and Reality)
  • Date & time 13th Jun 2024, at 14:00 UK time
  • Format: In-person
  • Link to the event
  • Organiser: Lancaster Centre for Marketing Analytics and Forecasting
  • Description: Presenters: Ivan Svetunkov and Nikolaos Kourentzes Abstract: Proper statistical models play an important role in forecasting providing a holistic framework for time series analysis. Because of that they can be extended to include more components and can be efficiently used in various practical decisions. Forecasting methods on the other hand, are used just to solve […] Read More
Sky is the limit! Sky is the Limit: Bringing the Exponential Smoothing to the Next Level
  • Date & time 30th Jun 2024, at 09:00 CET
  • Format: In-person
  • Link to the event
  • Organiser: International Institute of Forecasters
  • Description: Instructors: Ivan Svetunkov and Kandrika Pritularga Abstract: 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 […] Read More
Why you should care about exponential smoothing: data scientists perspective
  • Date & time 9th Aug 2024, at 14:00 UK time
  • Format: Online
  • Link to the event
  • Organiser: sktime
  • Description: Abstract: In the age of Machine Learning, many data scientists have started using more complicated approaches for forecasting, such as XGBoost, k-NN, Artificial Neural Networks etc. Due to the increased interest in these methods, the simpler and robust approaches might look less attractive than the more innovative ones and as a result tend to be […] Read More