# description
Input data model for training a forecasting model. Includes target series with ground truth, optional exogenous variables, location, and model training configuration. Aligned with SAREF and UncertML concepts.

# properties.forecast_type.description
Type of forecast model to train: deterministic (point) or stochastic (probabilistic).

# properties.time_parameters.description
Target forecast resolution and horizon the model should learn.

# properties.model_config.description
Configuration parameters for the training process.

# properties.model_config.properties.model_id.description
Unique identifier for this model instance.

# properties.model_config.properties.model_preset.description
ML algorithm or architecture. If not defined it would be Chronos2Model by default. It also can be any of the preset (training all the available models), best_quality, high_quality, medium_quality, fast_training. Model list https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-model-zoo.html#module-autogluon.timeseries.models.

# properties.model_config.properties.model_version.description
Version identifier for the model (e.g., '1.0.0').

# properties.model_config.properties.training_split.properties.train_ratio.description
Fraction of data used for training (e.g., 0.70).

# properties.model_config.properties.training_split.properties.validation_ratio.description
Fraction of data used for validation (e.g., 0.15).

# properties.model_config.properties.training_split.properties.test_ratio.description
Fraction of data used for testing (e.g., 0.15).

# properties.model_config.properties.lookback_steps.description
Number of past timesteps used as input features for the model.

# properties.model_config.properties.loss_function.description
Loss function to optimize (e.g., 'mse', 'mae', 'pinball', 'negative_log_likelihood').

# properties.model_config.properties.quantile_levels.description
Quantile levels to predict (for quantile regression models).

# properties.target_series.description
The target variable with full historical ground truth data for training.

# properties.target_series.properties.variable_name.description
Name of the variable to learn to forecast.

# properties.target_series.properties.asset_id.description
Identifier of the asset this series belongs to.

# properties.target_series.properties.asset_type.description
Type of the asset (e.g., 'PV', 'WindTurbine', 'Load').

# properties.target_series.properties.training_values.description
Full historical ground truth time series used for training the model.

# properties.location.description
Geographic location of the asset. Aligned with WGS84.

# properties.exogenous_series.description
Optional additional time series of external variables aligned with the target series time range.

# properties.exogenous_series.items.properties.series_id.description
Unique identifier for this exogenous series.

# properties.exogenous_series.items.properties.training_values.description
Historical values aligned with target_series time range.