Function reference
Preprocessing
Data curation, outlier detection, and split tools.
| preprocessing._binner | QuantileBinner class for binning data into quantile-based bins. |
| preprocessing._common | Common preprocessing functions and utilities. |
| preprocessing._differentiator | Time series differentiator transformer. |
| preprocessing._rolling | |
| preprocessing.outlier | Outlier detection utilities (legacy wrapper for spotforecast2_safe). |
| preprocessing.outlier_plots | |
| preprocessing.split | |
| preprocessing.time_series_visualization | Time series visualization. |
Model Selection
Search algorithms and cross-validation tools.
| model_selection.bayesian_search | Bayesian hyperparameter search functions for forecasters using Optuna. |
| model_selection.grid_search | |
| model_selection.random_search | Random search hyperparameter optimization for forecasters. |
| model_selection.split_base | Base class for time series cross-validation splitting. |
| model_selection.split_ts_cv | Time series cross-validation splitting. |
| model_selection.spotoptim_search | Hyperparameter search functions for forecasters using SpotOptim. |
| model_selection.utils_common | Common validation and initialization utilities for model selection. |
| model_selection.utils_metrics | Metrics calculation utilities for model selection. |
Manager
Model management and configurators.
| manager.models.forecaster_recursive_model_full | Full-featured base forecasting model with Bayesian tuning and SHAP. |
| manager.models.forecaster_recursive_lgbm_full | LGBM forecaster with real Bayesian tuning and SHAP. |
| manager.models.forecaster_recursive_xgb_full | XGBoost forecaster with real Bayesian tuning and SHAP. |
| manager.plotter | Module for generating interactive prediction plots. |
| manager.trainer_full | Module for managing full model training. |
| manager.multitask.agg_predictor | Aggregate per-target prediction packages into a weighted forecast. |
| manager.multitask.BaseTask | Shared base for all multi-target forecasting pipeline tasks. |
| manager.multitask.LazyTask | Task 1 — Lazy Fitting with default LightGBM parameters. |
| manager.multitask.OptunaTask | Task 3 — Optuna Bayesian hyperparameter tuning. |
| manager.multitask.SpotOptimTask | Task 4 — SpotOptim surrogate-model Bayesian tuning. |
| manager.multitask.PredictTask | Task 5 — Predict-only using previously saved models. |
| manager.multitask.CleanTask | Cache-cleaning task — removes all cached data from the pipeline cache. |
| manager.multitask.MultiTask | Orchestrates a multi-target time-series forecasting pipeline. |
| manager.multitask.run | Run the MultiTask forecasting pipeline and return predictions. |
Forecaster
Forecasting utilities and metrics.
| forecaster.metrics | Metrics for evaluating forecasting models. |
| forecaster.utils | |
| forecaster.recursive._warnings |
Stats
Statistical analysis tools.
| stats.autocorrelation |
Tasks
Demonstration and predefined forecasting tasks.
| tasks.task_demo | Task demo: compare baseline, covariate, and custom LightGBM forecasts against ground truth. |
| tasks.task_entsoe | Unified CLI task script for ENTSO-E data downloading, model training, and prediction. |
| tasks.task_n_to_1 | |
| tasks.task_n_to_1_dataframe | |
| tasks.task_n_to_1_with_covariates | N-to-1 Forecasting with Exogenous Covariates and Prediction Aggregation. |
| tasks.task_n_to_1_with_covariates_and_dataframe | N-to-1 Forecasting with Exogenous Covariates and Prediction Aggregation. |
Utils
Validation and data transformation utilities.
| utils.data_transform | Data transformation utilities for time series forecasting. |
| utils.forecaster_config | Forecaster configuration utilities. |
| utils.generate_holiday | Utilities for generating holiday dataframe as covariate. |
| utils.validation | Validation utilities for time series forecasting. |
Exceptions
Spotforecast exception classes.
| exceptions | Custom exceptions and warnings for spotforecast2. |