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CMAF FFT: Generation of Time Series Scenarios: How to Do It and Make it Pay
CMAF FFT: Making your model generalise better: Cross-validation and data augmentation
CMAF FFT: Accuracy, Explainability and Trust: the trinity of business forecasting
Hierarchical Forecasting for Demand in Supply Chains
CMAF FFT: What’s new in forecasting software by Oliver Schaer
How to forecast time series with hierarchies.
CMAF FFT: Alternatives to judgmental forecast adjustments: a retail case study
CMAF FFT: Identifying sequential changes in mean and variance within more complex model structures
Hierarchical Forecasting in Python | Nixtla
CMAF FFT: Machine Learning forecasts – confirmation bias or value add
CMAF FFT: Human vs machine: do practitioners trust forecasting algorithms
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Last Updated: October 3, 2026
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Summary
This is the second webinar in the Season 4 of "Friday This is the ninth webinar in the series of "Friday This is the sixteenth webinar in the series of "Friday This is the third webinar in Season 2 of "Friday This is the fourteenth webinar in the series of "Friday This is the eleventh webinar in Season 2 of "Friday This is the tenth webinar in the series of "Friday How do you predict sales for 10000 products at 10000 stores every day for two years into the future? That's over a billion ... Some timeseries can be structured in hierarchies (e.g. the revenue of a clothing brand is the sum of the sales of in all of its stores). This is the first webinar in Season 3 of "Friday A vast amount of time series datasets are organized into structures with different levels or hierarchies of aggregation. In this talk ... This is the fourth webinar in the 3 Season of "Friday This is the sixth webinar in Season 2 of "Friday
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