<forecast_context>
<dataset>
- Observations: 100
- Series: 1
- Frequency: D
- Date range: 2023-01-01 to 2023-04-10
- Target: sales
- Exogenous columns: promo
- 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']. 1 exogenous variable available as predictors.
- 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_plan>
- Forecaster: ForecasterFoundation
- Steps: 5
- Estimator: Salesforce/moirai-2.0-R-small
- Primary metric: mean_absolute_error
- Chosen by the user instead of the rules: forecaster, estimator
- Plan: ForecasterFoundation + Salesforce/moirai-2.0-R-small. No lag or window features: the foundation model forecasts directly from the raw context window. MAE is interpretable, robust to outliers, and works at any scale. The model reads up to 2048 observations of the series as context, so the whole history is used (the series has 100). Exogenous variables ['promo'] are not used: 'Salesforce/moirai-2.0-R-small' does not support covariates. The weights of 'Salesforce/moirai-2.0-R-small' are released under CC-BY-NC-4.0, which restricts commercial use (https://huggingface.co/Salesforce/moirai-2.0-R-small).

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 5 steps
- Files read: none
- Variables defined: predictions (no metrics: there is no ground truth yet)
- Packages imported: none
- Length: 2 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>
