Function reference

Data

Utilities for fetching and loading time series, weather, and holiday data.

data.fetch_data.fetch_data	Fetches a dataset from a CSV file or processes a DataFrame.
data.fetch_data.fetch_holiday_data	Fetches holiday data for the dataset period.
data.fetch_data.fetch_weather_data	Fetch weather data for the dataset period plus forecast horizon.
data.fetch_data.get_cache_home	Return the location where persistent models are to be cached.
data.fetch_data.get_data_home	Return the location where datasets are to be stored.
data.fetch_data.get_package_data_home	Return the location of the internal package datasets.
data.fetch_data.load_timeseries	Load the actual-load time series from interim/energy_load.csv.
data.fetch_data.load_timeseries_forecast	Load the day-ahead forecast time series from interim/energy_load.csv.
data.data	Data structures for input and processed data.
data.demo_data	Demo dataset configuration for safety-critical forecasting tasks.
data.demo_loader	Demo data loader for safety-critical forecasting tasks.

Preprocessing

Hardened tools for data curation, resampling, outlier detection, feature engineering, and temporal train/test splitting.

preprocessing.curate_data.agg_and_resample_data	Aggregates and resamples the data to (e.g., hourly) frequency by computing the specified aggregation (e.g. for each hour).
preprocessing.curate_data.basic_ts_checks	Checks if the time series data has a datetime index and is sorted.
preprocessing.curate_data.curate_holidays	Checks if the holiday dataframe has the correct shape.
preprocessing.curate_data.curate_weather	Checks if the weather dataframe has the correct shape.
preprocessing.curate_data.get_start_end	Get start and end date strings for data and covariate ranges.
preprocessing.curate_data.remove_duplicate_timestamps	Resolve duplicate timestamps across all data columns.
preprocessing.curate_data.reset_index	Resets the index of the dataframe and assigns a name to the index column.
preprocessing.outlier.get_outliers	Detect outliers in each column using Isolation Forest.
preprocessing.outlier.manual_outlier_removal	Manual outlier removal function.
preprocessing.outlier.mark_outliers	Marks outliers as NaN in the dataset using Isolation Forest.
preprocessing.checking.check_exog	Validate that exog is a pandas Series or DataFrame.
preprocessing.checking.check_exog_dtypes	Check that exogenous variables have valid data types (int, float, category).
preprocessing.checking.check_interval	Validate that a confidence interval specification is valid.
preprocessing.checking.check_predict_input	Check all inputs of predict method. This is a helper function to validate
preprocessing.checking.check_residuals_input	Check residuals input arguments in Forecasters.
preprocessing.checking.check_y	Validate that y is a pandas Series without missing values.
preprocessing.checking.get_exog_dtypes	Extract and store the data types of exogenous variables.
preprocessing.checking.set_cpu_gpu_device	Set the device for the estimator to either ‘cpu’, ‘gpu’, ‘cuda’, or None.
preprocessing.exog_builder.ExogBuilder	Builds a set of exogenous features for a given date range.
preprocessing.imputation.apply_imputation	Apply imputation to a DataFrame based on the method specified in config.
preprocessing.imputation.WeightFunction	Callable class for sample weights that can be pickled.
preprocessing.imputation.custom_weights	Return 0 if index is in or near any gap.
preprocessing.imputation.get_missing_weights	Return imputed DataFrame and a series indicating missing weights.
preprocessing.data_transform	Data transformation utilities for time series forecasting.
preprocessing.forecaster_config	Forecaster configuration utilities.
preprocessing.linearly_interpolate_ts	Linear interpolation transformer for time series data.
preprocessing.repeating_basis_function	Repeating Basis Function transformer for cyclical features.
preprocessing.rolling.RollingFeatures	Compute rolling window statistics over time series data.

Processing

Utilities for aggregated and n-to-n predictions.

processing.agg_predict.agg_predict	Aggregates multiple prediction columns into a single combined prediction series.
processing.n2n_predict.n2n_predict	End-to-end baseline forecasting using equivalent date method.
processing.n2n_predict_with_covariates.n2n_predict_with_covariates	End-to-end recursive forecasting with exogenous covariates.

Forecaster

Recursive forecasting classes, seasonal baselines, and metrics.

forecaster.base	ForecasterBase class.
forecaster.recursive._forecaster_recursive.ForecasterRecursive	Recursive autoregressive forecaster for scikit-learn compatible estimators.
forecaster.recursive._forecaster_equivalent_date.ForecasterEquivalentDate	This forecaster predicts future values based on the most recent equivalent
forecaster.recursive._forecaster_recursive_multiseries
forecaster.utils
forecaster.metrics.add_y_train_argument	Add y_train argument to a function if it is not already present.
forecaster.metrics.calculate_coverage	Calculate coverage of a given interval.
forecaster.metrics.create_mean_pinball_loss	Create pinball loss for a given quantile.
forecaster.metrics.crps_from_predictions	Compute the Continuous Ranked Probability Score (CRPS) from predictions.
forecaster.metrics.crps_from_quantiles	Calculate the Continuous Ranked Probability Score (CRPS) from quantiles.
forecaster.metrics.mean_absolute_scaled_error	Mean Absolute Scaled Error (MASE).
forecaster.metrics.root_mean_squared_scaled_error	Root Mean Squared Scaled Error (RMSSE).
forecaster.metrics.symmetric_mean_absolute_percentage_error	Compute the Symmetric Mean Absolute Percentage Error (SMAPE).
forecaster.wrappers.model	Recursive forecaster model wrappers for different estimators.
forecaster.wrappers.lgbm	Recursive forecaster wrapper using LightGBM.
forecaster.wrappers.xgb	Recursive forecaster model wrappers for different estimators.

Splitter

Time-series-aware cross-validation folds and train/val/test holdout helpers.

splitter.split_base	Base class for time series cross-validation splitting.
splitter.split_one_step	One step ahead cross-validation splitting.
splitter.split_ts_cv	Time series cross-validation splitting.
splitter.split.split_abs_train_val_test	Splits a time series DataFrame into training, validation, and test sets based on absolute timestamps.
splitter.split.split_rel_train_val_test	Splits a time series DataFrame into training, validation, and test sets by percentages.
splitter.utils_common	Common validation and initialization utilities for model selection.

Backtesting

Backtesting orchestration for time series forecasters.

backtesting.validation.backtesting_forecaster	Backtesting of forecaster model following the folds generated by the TimeSeriesFold
backtesting.validation.backtesting_forecaster_one_step	Backtesting of forecaster model using one-step-ahead predictions.
backtesting.validation

Configurator

Task-level configuration objects for the forecasting pipelines.

configurator.config_entsoe.ConfigEntsoe	Configuration for the ENTSO-E forecasting pipeline.
configurator.config_multi.ConfigMulti	Configuration for the multi-input forecasting pipeline.

Manager

High-level training, prediction, and model persistence orchestration.

manager.demo_metrics.calculate_metrics	Calculate MAE and MSE for numeric evaluation.
manager.trainer.get_last_model	Get the latest trained model from the cache.
manager.trainer.get_path_model	Yield the path to a model file for a given iteration and model name.
manager.trainer.load_iteration	Load a saved model at a given iteration.
manager.predictor.build_prediction_package	Build a prediction package compatible with PredictionFigure.
manager.predictor.get_model_prediction	Get the prediction package from the latest trained model.
manager.logger	Audit-grade logging for spotforecast2-safe.
manager.persistence
manager.tools	Utility tools for command-line interface parsing.
manager.features.apply_cyclical_encoding	Apply cyclical (sine/cosine) encoding to periodic integer features.
manager.features.create_interaction_features	Append bilinear interaction terms to an exogenous feature matrix.
manager.features.select_exogenous_features	Select and deduplicate exogenous feature columns for model training.
manager.features.merge_data_and_covariates	Merge target data with exogenous features and split into train/predict slices.
manager.features.get_target_data	Extract the training series and exogenous slices for one target column.

Utils

General-purpose utility functions: CPE generation, validation, and data transforms.

utils.cpe.get_cpe_identifier	Generates the CPE 2.3 identifier for the spotforecast2-safe project.
utils.convert_to_utc	Utility functions for timezone conversion.

Security

Masking and PII-protection utilities for safe logging (CWE-532, CWE-312).

security.masking.mask_estimator

Return a non-sensitive string representation of an estimator.

Calendar

Calendar, holiday, and day/night feature engineering for covariate construction.

calendar.holiday.create_holiday_df	Create a DataFrame with datetime index and a binary holiday indicator column.
calendar.holiday.get_holiday_features	Build public-holiday indicators and align them to a regular time grid.
calendar.features.get_calendar_features	Create calendar-based features for a contiguous time range.
calendar.features.get_day_night_features	Create day/night features using astronomical sunrise and sunset times.

Weather

Weather data integration using the Open-Meteo API.

weather.client.WeatherClient	Client for fetching weather data from Open-Meteo API.
weather.client.WeatherService	High-level service for weather data generation.
weather.features.get_weather_features	Fetch weather data and compute rolling-window features.

Downloader

Data downloaders for external data sources (e.g. ENTSO-E).

downloader.entsoe.download_new_data	Download new load and forecast data from ENTSO-E.
downloader.entsoe.merge_build_manual	Merge all raw CSV files from the ‘raw’ directory into a single interim file.

Exceptions

Custom exception types for safety-critical failure signalling.

exceptions

Custom exceptions and warnings for spotforecast2.

Tasks

Executable tasks for demonstration and production pipelines.

tasks.task_safe_demo	Task demo: compare baseline, covariate, and custom LightGBM forecasts against ground truth.
tasks.task_safe_n_to_1_with_covariates_and_dataframe	N-to-1 Forecasting with Exogenous Covariates and Prediction Aggregation.