19 reasons to attend ISF 2021 (virtual - starting June 27)


International Symposium on Forecasting (virtual, June 27 - July 2)

IIF LogoThe 41st International Symposium on Forecasting will be virtual again this year, and begins Sunday June 27. SAS will have a huge presence -- as event sponsor, sponsor of the IIF/SAS Research Grants, and with 19 individual presentations.

ISF registration is free to members of the International Institute of Forecasters, and it is easy to Join the IIF. A one-year Premium membership costs $145 and includes hardcopy subscriptions (plus online access) to both IIF journals: the International Journal of Forecasting and Foresight: The International Journal of Applied Forecasting

Students can join for just $25 per year, which includes online-only access to both journals.

As a truly international event, presentations run literally around-the-clock from Monday June 28 through mid-day on Wednesday June 30. Special half-day Workshops run on Sunday June 27, as well as Thursday July 1 and Friday July 2. (Workshops require a $50 additional fee.)

In addition to the SAS presentations and the Practitioner Track listed below, here are several other sessions I'm looking forward to (all dates/times EDT). Be ready to get up early and stay up late:

  • Workshop: Deep Learning for Forecasting - Tim Januschowski & Lorenzo Stella (Sunday June 27, 14:00-18:00)
  • Accuracy, Explainability, and Trust in Business Forecasting - Simon Spavound (Monday June 28, 04:00-04:20)
  • Re-Analysis of Intermittent Demand Forecasting Methods - John Boylan (Monday June 28, 08:40-09:00)
  • Opening Welcome / Members Meeting - George Athanasopoulos & IIF Board of Directors (Monday June 28, 09:00-10:00)
  • A Picture is Worth a Thousand Data Points: An Image-Based Time Series Forecasting Approach - Artemios-Anargyros Semenoglou (Monday June 28, 15:00-15:20)
  • Size Does Matter: Timer Series Augmentation for Enhanced Cross-Learning - Evangelos Spiliotis (Monday June 28, 15:20-15:40)
  • Forecasting Uncertainty: The Quest for Quantification - Steve Morlidge (Monday June 28, 16:00-16:20)
  • Keynote: Humachine: Humankind, Machines, and the Future of the Enterprise - Nada Sanders (Monday June 28, 18:00-19:00)
  • Probabilistic Ensemble Forecasting of Australian COVID-19 Cases - Rob Hyndman (Monday June 28, 21:40-22:00)
  • Some Theoretical Ways in which FVA Analysis Can Be Misleading and How This Can Be Remedied - Paul Goodwin (Tuesday June 29, 05:20-05:40)
  • Stylised Facts of FVA, a Meta-Analysis Where Do Judgmental Adjustments Improve Accuracy? - Robert Fildes (Tuesday June 29, 05:40-06:00)
  • Algorithm Aversion or Algorithm Appreciation? - Shari De Baets (Tuesday June 29, 06:00-06:20)
  • Using Judgmental Forecasting and Scenario Thinking for Anticipating the Future - George Wright (Tuesday June 29, 06:20-06:40)
  • Judgmental Interventions: Model Tuning and Forecast Adjustments in a Retailing Case Study - Anna Sroginis (Tuesday June 29, 06:40-07:00)
  • Evaluating the Impact of Business Practices on Inventory Performance - Evangelos Theodorou (Tuesday June 29, 08:20-08:40)
  • New Product Life-Cycle Forecasting with Temporal Hierarchies - Oliver Schaer (Tuesday June 29, 08:20-08:40)
  • Demand Forecasting Under Lost-Sales Stock Policies - Juan Trapero (Tuesday June 29, 08:40-09:00)
  • Fast and Frugal Time Series Forecasting - Fotios Petropoulos (Tuesday June 29, 11:40-12:00)
  • Prediction Intervals: Neglected Diagnostics? - Keith Ord (Tuesday June 29, 16:00-16:20)
  • Estimating Interval Forecasts using Pruned Ensembles - Erick Meira (Tuesday June 29, 16:20-16:40)
  • General NN Forecaster - Slawek Smyl (Tuesday June 29, 16:20-16:40)
  • Forecast Combinations, Pooling, and Hierarchies: How do They Combine? - Nikos Kourentzes (Tuesday June 29, 17:40-18:00)
  • Forecasting for Social Good - Bahman Rostami-Tabar (Wednesday June 30, 08:40-09:00)
  • Keynote: Forecasting Climate Change, Pandemics and Econometrics - David Hendry (Wednesday June 30, 10:00-11:00)
  • Workshop: Forecasting to Meet Demand - Stephan Kolassa & Roland Martin (Thursday July 1, 14:00-18:00)
  • Workshop: Business Forecasting: Techniques, Application and Best Practices - Eric Stellwagen & Sarah Darin (Thursday July 1, 14:00-18:00)
  • Workshop: Evaluating Forecasting Performance - Evangelos Spiliotis (Friday July 2, 14:00-18:00)

SAS Presentations (date/time is EDT)

Monday June 28

Session: Demand Forecasting 2 (06:00-07:00)

  • Demand Forecasting in Times of COVID - Michel Kurcewicz (06:40-07:00)

Session: Open Source Forecasting in SAS (11:00 - 12:20)

  • Accelerate Open Source Forecasting with SAS (Part 1) - Jessica Curtis
  • Accelerate Open Source Forecasting with SAS (Part 2) - Andrea Moore
  • Deep Learning for Retail Sales Forecasting - Szymon Haponiuk
  • Major Paradigm Shifts in Modern Forecasting Methodology - Russ Wolfinger

Practitioner Track: Large-Scale New Product Forecasting: A ML-Based Approach - Nitzi Roehl (13:00-13:30)

Session: COVID-19 Forecasting: Exploring and Modeling Data (14:00-15:20)

  • Location Network Analysis and Supervised ML Models to Identify VIrus Spread Trends - Carlos Pinheiro
  • Evaluation of Statistical Models for Producing Weekly COVID-19 Forecast - Ran Bi
  • Representing and Forecasting COVID-19 Pandemic Using Differential Equation Models - Marc Kessler
  • Visualization by Pattern Similarity for COVID-19 Data Set - Youngjin Park

Session: Forecasting and Uncertainty (16:00-16:40)

  • The Forecaster's Predicament: Issues with Communicating Uncertainty - Mike Gilliland (16:20-16:40)

Session: Forecasting and Software (17:00-18:00)

  • Scalable Cloud-Based Automatic Time Series Imputation - Thiago Quirino
  • Forecasting Software Trends for the Next Decade - Michele Trovero
  • Using Open Source Machine Learning Algorithms in SAS Visual Forecasting - Javier Delgado

Tuesday June 29

Session: Time Series Clustering for Forecasting (02:00-03:00)

  • Time Series Segmentation Using Two-Stage Clustering Approach - Sagar Mainkar (02:00-02:20)

Session: Retail Forecasting 2 (09:00-10:00)

  • Enhancing Short-Term Demand Sensing Using Machine Learning - Charles Chase (09:20-09:40)

Session: Automated Forecasting (11:00-12:00)

  • Monitoring Forecast Model Fitness Using Control Charts - Joe Katz (11:20-11:40)

Session: Neural Networks 2 (16:00-17:00)

  • Time Series Forecasting with Time Series Plot and Computer Vision - Taiyeong Lee (16:40-17:00)

Friday July 2

Workshop: Formulating State Space Models - Rajesh Selukar (14:00-18:00)

Practitioner Track

Again this year the ISF will feature a special Practitioner Track of 30-minute presentations from ten top contributors to the field (see the link for session times, titles, and abstracts):

  • Alla Anashenkova, ThroughPut Inc
  • Patrick Bower, Combe International
  • Simon Clarke, Argon & Co
  • Ran Ding, Google
  • Stefa Etchegaray Garcia, IBM
  • Jonathon Karelse, NorthFind Management
  • Sara Park, Coca-Cola
  • Nitzi Roehl, SAS
  • Niels van Hove, Aera Technology
  • Dawn Woodard, Uber

Register here to attend (virtual) ISF 2021. As you can see, there are a lot more than 19 good reasons to attend!


About Author

Mike Gilliland

Product Marketing Manager

Michael Gilliland is a longtime business forecasting practitioner and currently Product Marketing Manager for SAS Forecasting. He is on the Board of Directors of the International Institute of Forecasters, and is Associate Editor of their practitioner journal Foresight: The International Journal of Applied Forecasting. Mike is author of The Business Forecasting Deal (Wiley, 2010) and editor of the free e-book Forecasting with SAS: Special Collection (SAS Press, 2020). He is principal editor of Business Forecasting: Practical Problems and Solutions (Wiley, 2015) and Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). In 2017 Mike receive the Institute of Business Forecasting's Lifetime Achievement Award. In 2021 his paper "FVA: A Reality Check on Forecasting Practices" was inducted into the Foresight Hall of Fame. Mike initiated The Business Forecasting Deal blog in 2009 to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.

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