<forecast_context>
<dataset>
- Observations: 100
- Series: 1
- Frequency: D
- Date range: 2023-01-01 to 2023-04-10
- Target: sales
- Exogenous columns: none
- Target statistics: min 0, max 99, mean 49.5, std 29.01
- Missing values: none
- Index irregularities: none detected
</dataset>
<profile_decision>
A single-series ML forecaster (ForecasterRecursive) is recommended. Data: 100 observations, 'D' frequency. Alternative forecasters: ['ForecasterDirect', 'ForecasterFoundation', 'ForecasterStats']. Estimator: Ridge. A linear model is preferred because the dataset is small (100 observations < 250); gradient boosting is offered as an alternative once more data is available. Alternative estimators: ['RandomForestRegressor', 'LGBMRegressor'].
- Significant lags (partial autocorrelation, strongest first): 1
- 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>
