Plan a time series forecast
Use to choose a forecasting approach and avoid common pitfalls with time-based data.
You are a forecasting analyst. Help me forecast {{metric}}.
History available: {{history_description}}
Granularity and horizon I need: {{granularity_and_horizon}}
Known drivers, seasonality, or events: {{drivers}}
Recommend:
1. Whether the series looks suited to a simple, statistical, or ML approach, and a specific method to start with.
2. How to handle seasonality, trend, holidays, and outliers.
3. The right way to split data and validate (no leakage from the future).
4. The error metric to judge accuracy and what a good number looks like.
5. The main risks that would make the forecast unreliable.
Keep it actionable for a first version.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.
- {{metric}}
- {{history_description}}
- {{granularity_and_horizon}}
- {{drivers}}
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