Comments (1)
Buenas @GabrielCornejo
Te recomiendo echarle un ojo a la user guide de hyperparameter tuning, tienes muchos ejemplos de cómo introducir cualquier hiperparámetro:
https://skforecast.org/latest/user_guides/hyperparameter-tuning-and-lags-selection
y a la de backtesting para entender la estrategia de validación que se usa para esta búsqueda:
https://skforecast.org/latest/user_guides/backtesting
La y
hace referencia a la propia serie temporal del problema de forecasting e initial_train_size
es el número de observaciones de esa serie que se emplean en el entrenamiento inicial antes de aplicar backtesting. Todas estas definiciones las puedes encontrar en la propia docstring de la función o en la sección de API de la docu:
Espero que te ayude
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Related Issues (20)
- `ForecasterAutoreg` fails to fit when `exog` do not have string column names HOT 1
- `ForecasterAutoreg` fails to fit when index do not start from 0 HOT 3
- How to use it with planning input or other forecast as a guide? HOT 5
- grid_search_sarimax stuck without any progress HOT 1
- Back testing HOT 1
- Backrest and hyper parameter tuning HOT 1
- Just a question about probabilistic forecasting HOT 3
- Naming convention for backtesting methods HOT 2
- Feature request regarding time series with different lengths HOT 5
- bayesian_search_forecaster (Optuna) & Saving/Resuming Study with RDB Backend HOT 1
- Feature request: Recursive multistep multivariate forecasting and direct multistep multi-series forecasting HOT 4
- A single model multivariate forecaster HOT 3
- Issue saving ForecasterSarimax object HOT 4
- Custom predictors are inefficient for window features HOT 1
- grid_search_sarimax takes a very long time to run HOT 1
- Feature request: Add ability to skip steps in backtesting HOT 6
- Bad error handling when Index is neither RangeIndex nor DateIndex HOT 3
- Why do i have faster runtime when using more frequent refits HOT 2
- syntax error !! HOT 1
- Feature request: Custom predictors for multivariate forecasting
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