<forecast_context>
<dataset>
- Observations: 90
- Observations pooled across series: 264
- Series: 3
- Frequency: D
- Date range: 2023-01-01 to 2023-03-31
- Target: sales
- Exogenous columns: promo, weekday
- Categorical exogenous columns: weekday
- Target statistics (store_a): min 92.61, max 106.4, mean 99.88, std 3.7
- Target statistics (store_b): min 103.1, max 116.3, mean 109.9, std 3.718
- Target statistics (store_c): min 113, max 126.2, mean 119.9, std 3.85
- Missing values: none
- Index irregularities: none detected
- Data warning: Series ending early: 1 series ends before the last date with a value (2023-03-31): 'store_c' (2023-03-25). ForecasterRecursiveMultiSeries does not predict them, and ForecasterFoundation predicts each one after its own last row, rows without a value included.
</dataset>
<profile_decision>
The dataset contains 3 series, so a multi-series forecaster (ForecasterRecursiveMultiSeries) is recommended. Data: 264 observations pooled across 3 series, 'D' frequency. Alternative forecasters: ['ForecasterFoundation']. Estimator: LGBMRegressor. A gradient boosting model is preferred for a dataset of this size (264 observations). Alternative estimators: ['XGBRegressor', 'Ridge']. 2 exogenous variables (1 categorical) available as predictors.
- Significant lags for store_a (partial autocorrelation, strongest first): 2, 1, 3, 4
- Significant lags for store_b (partial autocorrelation, strongest first): 2, 3, 1
- Significant lags for store_c (partial autocorrelation, strongest first): 2, 1, 3
- Suggested window features: mean(window=3), std(window=3), mean(window=7), mean(window=21)
- Suggested calendar features: day_of_week, weekend, month
</profile_decision>
</forecast_context>
