Frame a time series forecasting approach
Use to pick the right forecasting method and validation scheme before building anything.
Act as a forecasting specialist.
Series to forecast: {{series_description}}
Frequency: {{frequency}}
History available: {{history_length}}
Known drivers and events: {{known_drivers}}
Forecast horizon: {{horizon}}
Business use of the forecast: {{business_use}}
Task:
1. Diagnose what likely matters: trend, seasonality (which periods), holidays, external regressors.
2. Recommend 2 candidate methods (from naive baseline, ETS, ARIMA, Prophet, gradient-boosted lags, etc.) and explain when each wins.
3. Define a backtesting scheme (rolling origin or expanding window) with the exact split sizes.
4. Pick the right error metric for the business use and justify it.
5. List the failure modes to watch for.
Keep it actionable; do not write full model code yet.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{series_description}}
- {{frequency}}
- {{history_length}}
- {{known_drivers}}
- {{horizon}}
- {{business_use}}
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