<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_plan>
- Forecaster: ForecasterRecursiveMultiSeries
- Steps: 7
- Estimator: LGBMRegressor
- Lags: [1, 2, 3, 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
- Preprocessing steps:
  - [informational] Categorical exogenous variables detected: ['weekday']. These are handled automatically by skforecast (categorical_features='auto').
- Plan: ForecasterRecursiveMultiSeries + LGBMRegressor. Lags: [1, 2, 3, 7]. Window features: ['mean(window=3)', 'std(window=3)', 'mean(window=7)', 'mean(window=21)']. Calendar features: ['day_of_week', 'weekend', 'month'] (raw ordinal encoding). Exogenous variables included. 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>
<script>
- Mode: prediction: trains on all the data and forecasts the next 7 steps
- Files read: data.csv, exog_future.csv
- Variables defined: predictions (no metrics: there is no ground truth yet)
- Packages imported: lightgbm, pandas, skforecast
- Length: 69 lines
The script is available to the user as `result.code`; describe it from this summary and the plan, do not reproduce it. It has not been run: for its predictions, the user runs it or calls `assistant.forecast()`.
</script>
</forecast_context>
