<forecast_context>
<dataset>
- Observations: 100
- Series: 2
- Frequency: D
- Target: value
- Exogenous columns: none
</dataset>
<profile_decision>
The dataset contains 2 series, so a multi-series forecaster (ForecasterRecursiveMultiSeries) is recommended. Data: 200 observations, 'D' frequency. Alternative forecasters: ['ForecasterDirectMultiVariate']. Estimator: Ridge. A linear model is preferred because the dataset is small (200 observations < 250); gradient boosting is offered as an alternative once more data is available. Alternative estimators: ['RandomForestRegressor', 'LGBMRegressor'].
</profile_decision>
<forecast_plan>
- Steps: 3
- Estimator: Ridge
- Lags: [1, 2, 3, 4, 5, 7]
- Window features: [{'stats': ['mean', 'std'], 'window_size': 3}, {'stats': ['mean'], 'window_size': 7}, {'stats': ['mean'], 'window_size': 21}]
- Primary metric: mean_absolute_scaled_error
- Plan: ForecasterRecursiveMultiSeries + Ridge. Lags: [1, 2, 3, 4, 5, 7]. Window features: ['mean(window=3)', 'std(window=3)', 'mean(window=7)', 'mean(window=21)']. Calendar features: ['day_of_week', 'weekend', 'month'] (cyclical encoding). NaN rows kept (NaN-tolerant estimator). MASE is scale-independent, enabling fair comparison across differently-scaled series.

Note: A validated Python script implementing this plan is generated separately. Do not generate code yourself.
</forecast_plan>
<evaluation_metrics>
 series  MAE   MSE  MASE
store_a  2.5  9.25   0.8
store_b  3.5 16.50   1.2
</evaluation_metrics>
<predictions>
Total rows: 6 (all shown below).
              level       pred
2023-04-11  store_a  1000.0000
2023-04-12  store_a  1111.1111
2023-04-13  store_a  1250.0000
2023-04-11  store_b  1500.0000
2023-04-12  store_b  1750.0000
2023-04-13  store_b  2000.0000
</predictions>
</forecast_context>
