AstraZeneca/judgyprophet
Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).
Implements a Stan-based Bayesian model that encodes business judgments about future events as informative priors, then updates these priors with observed data post-event using standard Bayesian inference. Supports trend and level events that can occur outside the training window, allowing forecasts to reflect anticipated business impacts (e.g., market launches, price changes) before they happen. Built on Prophet's architecture but replaces point estimates with posterior distributions, enabling principled balance between historical patterns and expert judgment.
No commits in the last 6 months. Available on PyPI.
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58
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Language
Python
License
Apache-2.0
Category
Last pushed
Apr 14, 2022
Commits (30d)
0
Dependencies
4
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