CMAF FFT: Generation of Time Series Scenarios: How to Do It and Make it Pay
Channel: Lancaster CMAF
Date: 2021-05-03

This is the fourteenth webinar in the series of "Friday Forecasting Talks", hosted by Centre for Marketing Analytics and Forecasting of Lancaster University, UK.
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-- Contents of this video --------------------------
00:00​​​ - Introduction
02:15  - Start of the presentation
08:41 - Four types of scenarios
16:11 - Scenarios generation for intermittent demand
32:16 - Scenarios for Inventory optimisation
35:10 - Conclusions
37:26 - Q&A start. When parametric can be more efficient than bootstrap?
39:00 - The assumptions of Markov bootstrap
41:37 - Data length in Markov bootstrap
44:16 - Seasonality in intermittent demand
46:15 - Autocorrelations in intermittent demand
48:03 - Correlated series in intermittent demand
51:47 - Slow trends in intermittent demand
54:35 - The future of intermittent demand

The abstract: Time series scenarios are artificial data series intended as inputs to decision processes. Those processes may involve forecasting, system design, or operator training. We outline criteria for evaluating scenario generators, explain how to create scenarios using nonparametric bootstrapping, and show how to exploit scenarios at the intersection of forecasting and inventory optimization.

Dr. Tom Willemain is Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute, Cofounder and Senior Vice President of Smart Software, Inc., and former intelligence officer. He holds degrees from Princeton University and Massachusetts Institute of Technology.

Images by https://storyset.com/​