processing.n2n_predict.n2n_predict

processing.n2n_predict.n2n_predict(
    data=None,
    columns=None,
    forecast_horizon=24,
    contamination=0.01,
    window_size=72,
    force_train=True,
    model_dir=None,
    verbose=True,
    show_progress=True,
)

End-to-end baseline forecasting using equivalent date method.

This function implements a complete forecasting pipeline that: 1. Loads and validates target data 2. Detects and removes outliers 3. Imputes missing values 4. Splits into train/validation/test sets 5. Trains or loads equivalent date forecasters 6. Generates multi-step ahead predictions

Models are persisted to disk following scikit-learn conventions using joblib. By default, models are retrained (force_train=True). Set force_train=False to reuse existing cached models.

Parameters

Name	Type	Description	Default
data	Optional[pd.DataFrame]	Optional DataFrame with target time series data. If None, fetches data automatically. Default: None.	None
columns	Optional[List[str]]	List of target columns to forecast. If None, uses all available columns. Default: None.	None
forecast_horizon	int	Number of time steps to forecast ahead. Default: 24.	24
contamination	float	Contamination parameter for outlier detection. Default: 0.01.	0.01
window_size	int	Rolling window size for gap detection. Default: 72.	72
force_train	bool	Force retraining of all models, ignoring cached models. Default: True.	True
model_dir	Optional[Union[str, Path]]	Directory for saving/loading trained models. If None, uses cache directory from get_cache_home(). Default: None (uses ~/spotforecast2_cache/forecasters).	None
verbose	bool	Print progress messages. Default: True.	True
show_progress	bool	Show progress bar during training and prediction. Default: True.	True

Returns

Name	Type	Description
	pd.DataFrame	Tuple containing:
	Dict	- predictions: DataFrame with forecast values for each target variable.
	Tuple[pd.DataFrame, Dict]	- forecasters: Dictionary of trained ForecasterEquivalentDate objects keyed by target.

Raises

Name	Type	Description
	ValueError	If data validation fails or required data cannot be retrieved.
	ImportError	If required dependencies are not installed.
	OSError	If models cannot be saved to disk.

Examples

Basic usage with automatic model caching:

>>> predictions, forecasters = n2n_predict(
...     forecast_horizon=24,
...     verbose=True
... )
>>> print(predictions.shape)
(24, 11)

Load cached models (if available):

>>> predictions, forecasters = n2n_predict(
...     forecast_horizon=24,
...     force_train=False,
...     model_dir="./saved_models",
...     verbose=True
... )

Force retraining and update cache:

>>> predictions, forecasters = n2n_predict(
...     forecast_horizon=24,
...     force_train=True,
...     model_dir="./saved_models",
...     verbose=True
... )

With specific target columns:

>>> predictions, forecasters = n2n_predict(
...     columns=["power", "energy"],
...     forecast_horizon=48,
...     force_train=False,
...     verbose=True
... )

Notes

• Trained models are saved to disk using joblib for fast reuse.
• When force_train=False, existing models are loaded and prediction proceeds without retraining. This significantly speeds up prediction for repeated calls with the same configuration.
• The model_dir directory is created automatically if it doesn’t exist.
• Default model_dir uses get_cache_home() which respects the SPOTFORECAST2_CACHE environment variable.

Performance Notes

• First run: Full training (~2-5 minutes depending on data size)
• Subsequent runs (force_train=False): Model loading only (~1-2 seconds)
• Force retrain (force_train=True): Full training again (~2-5 minutes)