<forecast_context>
<dataset>
- Observations: 204
- Series: 1
- Frequency: MS
- Date range: 1991-07-01 to 2008-06-01
- Target: x
- Exogenous columns: none
- Target statistics: min 0.3362, max 1.257, mean 0.7682, std 0.2251
- Missing values: none
- Index irregularities: none detected
</dataset>
<profile_decision>
A single-series ML forecaster (ForecasterRecursive) is recommended. Data: 204 observations, 'MS' frequency. Alternative forecasters: ['ForecasterDirect', 'ForecasterFoundation', 'ForecasterStats']. Estimator: Ridge. A linear model is preferred because the dataset is small (204 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, 13, 12, 11, 10, 14, 9
- Suggested window features: mean(window=3), std(window=3), mean(window=12), mean(window=36)
- Suggested calendar features: month, quarter
</profile_decision>
<forecast_plan>
- Forecaster: ForecasterStats
- Steps: 12
- Estimator: Arima
- Primary metric: mean_absolute_error
- Chosen by the user instead of the rules: forecaster
- Plan: ForecasterStats + Arima. No lag or window features: the statistical model estimates its own autoregressive and seasonal structure. MAE is interpretable, robust to outliers, and works at any scale.
</forecast_plan>
<backtesting_strategy>
- steps: 12
- initial_train_size: 2003-04-01
- refit: True
- fixed_train_size: True
- gap: 0
- fold_stride: 12
- skip_folds: None
- allow_incomplete_fold: True
- differentiation: None
- n_folds: 6
- n_fits: 6
</backtesting_strategy>
<deterministic_summary>
Initial training up to 2003-04-01, fixed window, refit every fold (6 trainings), 12-step horizon, 6 folds. ForecasterStats is refitted in every fold whatever `refit` says: skforecast requires it for ARIMA models. Results: mean_absolute_error: 0.0612, mean_squared_error: 0.0061, mean_absolute_scaled_error: 0.8514.
</deterministic_summary>
<evaluation_metrics>
 mean_absolute_error  mean_squared_error  mean_absolute_scaled_error
              0.0612              0.0061                      0.8514
</evaluation_metrics>
<predictions>
Shape: 62 rows x 2 columns
Columns: ['fold', 'pred']
Folds: 6
  pred: min=0.6, max=0.905, mean=0.7525000000000001, std=0.09020809276334357
Index range: 2003-05-01 00:00:00 to 2008-06-01 00:00:00
</predictions>
</forecast_context>
