Decompose a time series into trend and seasonality
Use to separate trend, seasonal, and residual components and interpret each.
Act as a time series analyst.
Series: {{series_description}}
Frequency: {{frequency}}
Suspected seasonal periods: {{seasonal_periods}}
Task:
1. Recommend additive vs multiplicative decomposition for this series and explain how to decide.
2. Give Python code (statsmodels STL or seasonal_decompose) to perform the decomposition, assuming the data is a pandas Series named ts.
3. Explain how to read the trend, seasonal, and residual plots.
4. Show how to check whether the residual still has structure (autocorrelation) and what it means if it does.
5. Suggest what to do with the decomposition next (deseasonalize, detect anomalies, forecast).
Return code plus interpretation guidance.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}}
- {{seasonal_periods}}
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