How to Boost Your Forecast Accuracy with Event Modeling I always avoid prophesying beforehand because it is much better to prophesy after the event has already taken place. - Winston Churchill
Sarah Darin 20 years of experience with statistical consulting, sales forecasting, regression modeling and marketing analytics. Experience across a broad range of industries, including Consumer Packaged Goods, Telecommunications, Technology, Retail, Automotive and Finance. Undergraduate degree in Applied Mathematics from Harvard University and a Master s of Science in Statistics from the University of Chicago, where she also served as a Lecturer. 4-2
What We ll Cover Introductions Evolution of Forecasting Process What are Events? Forecasting Event-Driven Data Examples Summary Q&A 4-3
Evolution of Forecasting Process Phase 1 Phase 2 Phase 3 Judgment & Spreadsheets Automatic Time Series Approaches Customized Approaches 4-4
Customized Forecasting Methods Customized approaches can take many forms including: Event-index models Hierarchical approaches (e.g., top-down or other allocation schemes) Dynamic regression Trading day adjustments etc. 4-5
Common Events Product promotions Moveable holidays (e.g., Easter, Rosh Hashanah, Ramadan) Catastrophes (e.g., earthquakes, hurricanes, 9/11) Labor strikes Acquisitions New legislation or regulations 4-6
Forecasting Event-Driven Data Judgmentally adjust history to remove impact. Separate base demand from event-driven demand. Use a time series extension model (e.g., event-index model, ARIMA intervention model, etc.). Use a multivariate model (e.g., regression). 4-7
What is an Event-Index Model? An extension of exponential smoothing. An index-based approach. The model introduces an additional smoothing weight and updating equation. The model requires an event schedule. 4-8
Event Schedule 4-9
Building an Event-Index Model You need to decide how many event types are needed. Each event type needs to have occurred historically. The schedule needs to include both the history and forecast periods. 4-10
Weekly Data Weekly data presents many challenges 52 or 53 weeks per year More moveable holidays Lack of continuity between periods from year to year Event-index models can be very useful when forecasting weekly data 4-11
Seasonal Simplification A standard seasonal exponential smoothing model for weekly data generates 52 seasonal indices this is a very complex view of the seasonal pattern. Event models can be used to simplify the seasonal pattern by lowering the number of seasonal indexes (e.g., each week could be assigned an event index corresponding to the month in which it falls). 4-12
Conclusions Time series methods do not perform well in the presence of events. An event-index model is an effective way of forecasting event-driven data. The strengths of event-index models are accuracy, ease of application and adaptability to changing dynamics. Event-index models are particularly useful when forecasting weekly data 4-13
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Questions? 4-17
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