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Prophet & PyMC Model Setup

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Linked challenge: Distressed CRE Capital Stack Recovery Forecasting Agent

Format
Code-aware
Lines
8
Sections
1
Linked challenge
Distressed CRE Capital Stack Recovery Forecasting Agent

Prompt source

Original prompt text with formatting preserved for inspection.

8 lines
1 sections
No variables
0 checklist items
import prophet
import pymc as pm
# Build combined time-series forecasting and Bayesian recovery modeling script
def forecast_property_noi(historical_df):
    model = prophet.Prophet(seasonality_mode='multiplicative')
    model.fit(historical_df)
    # Run Monte Carlo cash flow simulation
    pass

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.