meta, data, preprocessing, feature_engineering, filters, data_analysis, feature_analysis, feature_diagnostic, forecast_analysis, forecast_diagnostic, models, model_ensemble, model_selection, forecasting, metrics, tests, evaluation, window, interpretation, output, reporting
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package namespaces |
Top-level module namespaces. feature_diagnostic and forecast_diagnostic are compatibility aliases for the corresponding analysis modules. |
configure, get_config, get_option, reset_config, use_config, DEFAULT_RANDOM_SEED, StageDefaultScope, MetadataLevel
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macroforecast.meta
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Global package defaults and config types. |
DataBundle, DataSpec, RegimeDirection, SamePeriodPolicy, as_panel, attach_metadata, custom_dataset, metadata, panel_info, set_frequencies, spec, validate_panel
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macroforecast.data
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Canonical panel and metadata helpers. |
align_frequency, chow_lin_disaggregate, infer_frequencies, frequency_hardening_issues, availability_lag, same_period_predictors, define_regime
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macroforecast.data
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Frequency alignment/inference, Chow-Lin disaggregation, data availability, same-period predictor, and regime metadata policies. |
load_fred_md, load_fred_qd, load_fred_sd, load_fred_md_sd, load_fred_qd_sd
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macroforecast.data
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Dataset loaders. |
load_custom_csv, load_custom_parquet, list_vintages, combine
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macroforecast.data
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Custom loading, vintage discovery, and panel combination. |
reprocess, custom_preprocess, standardize_panel, PreprocessedData
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macroforecast.preprocessing
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Direct pandas preprocessing. |
PreprocessSpec, FittedPreprocessor, preprocess_spec, custom_preprocess_step
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macroforecast.preprocessing
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Runner-compatible preprocessing fit/transform specs. |
FeatureSet, FeatureSpec, FittedFeatureBuilder, build_features, feature_spec
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macroforecast.feature_engineering
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Aligned forecast matrices, metadata, and runner-compatible feature specs. |
direct_target, average_target, forward_average_target, path_targets
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macroforecast.feature_engineering
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Direct, forward-average, and path target construction. |
feature_matrix, compose_features
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macroforecast.feature_engineering
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Paper-style feature blocks and sequential feature composition. |
lag, mixed_frequency_lags, rolling_mean, moving_average_ladder, scale_features, pca_features, sparse_pca_chen_rohe_features, varimax_features, group_pca, maf_features, time_features, custom_features
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macroforecast.feature_engineering
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Core direct pandas feature transforms. |
transform_features, log_features, diff_features, log_diff_features, pct_change_features, cumsum_features, seasonal_lag, season_dummy, fourier_features, polynomial_features, interaction_features, hp_filter_features, hamilton_filter_features, savitzky_golay_features, wavelet_features, adaptive_ma_rf_features, asymmetric_trim_features, rank_space_features, moving_average_changes, align_reference_weights, weighted_aggregate, partial_least_squares_features, sliced_inverse_regression_features, dfm_features, variance_selection, correlation_selection, lasso_selection, lasso_path_selection, rfe_selection, boruta_selection, stability_selection, genetic_selection, select_features, feature_selection_requires_target, normalize_feature_selection_method, FeatureSelectionResult, random_projection_features, nystroem_features
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macroforecast.feature_engineering
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Additional transform, seasonal, expansion, filter, supervised aggregation, supervised factor, feature-selection, and kernel-approximation feature functions. |
lag_step, rolling_step, moving_average_step, marx_step, transform_step, seasonal_lag_step, season_dummy_step, fourier_step, time_step, polynomial_step, interaction_step, scale_step, pca_step, sparse_pca_chen_rohe_step, varimax_step, group_pca_step, maf_step, hamilton_step, random_projection_step, nystroem_step, partial_least_squares_step, sliced_inverse_regression_step, custom_step
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macroforecast.feature_engineering
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Reusable step dictionaries for compose_features and runner-safe feature_spec pipelines. Feature selection uses individual method names in step mappings instead of a generic step builder. |
pca_then_lags, lags_then_pca, moving_average_pca_lags
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macroforecast.feature_engineering
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Convenience composed feature callables. |
ModelFit, VolatilityFit, SavedModel, save_fit, load_fit
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macroforecast.models
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Fitted model result wrappers and low-level fit persistence. |
ols, ridge, nonneg_ridge, shrink_to_target_ridge, fused_difference_ridge, random_walk_ridge, tvp_ridge, lasso, elastic_net, adaptive_lasso, adaptive_elastic_net, group_lasso, sparse_group_lasso, bayesian_ridge, huber, kernel_ridge, knn, glmboost, pls, scaled_pca, supervised_pca, supervised_scaled_pca
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macroforecast.models
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Linear, penalized, grouped, time-varying ridge, kernel, nearest-neighbor, and supervised dimension-reduction models. |
supervised_aggregation, component_aggregation, rank_aggregation, assemblage_regression, albacore_components, albacore_ranks
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macroforecast.models
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Generic supervised aggregation and Albacore/assemblage wrappers. |
solve_nonnegative_ridge, solve_simplex_ridge, solve_target_shrinkage_ridge, solve_mean_aligned_ridge, solve_fused_difference_ridge
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macroforecast.models
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Low-level constrained aggregation solver helpers returning weight vectors. |
svr, linear_svr, nu_svr
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macroforecast.models
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Support-vector regression models. |
nn, lstm, gru, transformer, hemisphere_nn, density_hnn
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macroforecast.models
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Torch-backed neural-network and density-forecast regressors; require macroforecast[deep]. |
ar, arima, auto_arima, var, bvar_minnesota, bvar_normal_inverse_wishart, ets, holt_winters, theta_method, naive, seasonal_naive, random_walk_drift, stlf, far, favar
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macroforecast.models
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Time-series, Bayesian VAR, exponential-smoothing, and factor-augmented forecasting models. |
dfm_mixed_mariano_murasawa, dfm_unrestricted_midas, midas_almon, midas_beta, midas_step, restricted_midas, unrestricted_midas
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macroforecast.models
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Mixed-frequency dynamic-factor and MIDAS models. |
decision_tree, random_forest, extra_trees, gradient_boosting, mars, xgboost, lightgbm, lgb_plus, lgba_plus, catboost
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macroforecast.models
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Tree, spline, ML, and LGB+ hybrid regressors. |
quantile_regression_forest, macro_random_forest
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macroforecast.models
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Macro-specific tree models. |
LGBPlusRegressor, LGBAPlusRegressor
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macroforecast.models
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LGB+ competition and LGB^A+ alternating estimator classes. |
garch11, egarch, gjr_garch, tgarch, realized_garch
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macroforecast.models
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Volatility models. |
ModelSpec, ModelParameter, custom_model, get_model, list_model_specs, describe_model, model_search_space
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macroforecast.models
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Model-owned defaults and hyperparameter spaces. |
BaggingRegressor, BoogingRegressor, RandomSubspaceRegressor, StackingRegressor, SuperLearnerRegressor, MODEL_ENSEMBLE_BASE_ESTIMATORS, MODEL_ENSEMBLE_SPECS, bagging, subagging, random_subspace, stacking, super_learner, booging, custom_model_ensemble, get_model_ensemble, list_model_ensemble_bases, list_model_ensemble_specs, describe_model_ensemble, model_ensemble_search_space
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macroforecast.model_ensemble
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Fit-time model-composition callables, estimator classes, and specs. |
WindowSpec, EstimationWindow, ValWindow, TestWindow, AlignmentWindow, StagePolicy, Split
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macroforecast.window
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Forecast experiment and stage timing objects. |
from_cutoffs, estimation_expanding, estimation_rolling, estimation_fixed, val_last_block, val_poos, val_expanding, val_rolling_blocks, val_blocked_kfold, val_random_kfold, test_origins, alignment_drop_incomplete, alignment_keep_missing
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macroforecast.window
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Component window builders. |
last_block, poos, expanding, rolling_blocks, blocked_kfold, random_kfold
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macroforecast.window
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Shortcut window specs. |
stage_policy, custom_stage_policy, stage_index, stage_panel, last_block_split, poos_split, expanding_split, rolling_blocks_split, blocked_kfold_split, random_kfold_split, make_splitter, resolve_window, resolve_stage_policy, split_table, normalize_window_name
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macroforecast.window
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Stage timing, resolver helpers, and train/val split inspection. |
metrics
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macroforecast.metrics
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Forecast scoring namespace, including point scores and risk-adjusted forecast-return metrics. Use mf.metrics.rmse, not mf.rmse. |
tests
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macroforecast.tests
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Forecast-comparison test namespace, including mf.tests.custom_test, mf.tests.equal_predictive_tests, mf.tests.model_confidence_set, mf.tests.blocked_oob_reality_check, mf.tests.superior_predictive_ability_test, mf.tests.reality_check_test, mf.tests.stepm_test, interval coverage, and PIT diagnostics. Use mf.tests.dm_test, not mf.dm_test. |
EvaluationReport, evaluate_report, aggregate_scores, filter_oos_period, error_decomposition, benchmark_comparison, regime_scores
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macroforecast.evaluation
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Multi-slice evaluation reports, OOS filtering, error decomposition, benchmark comparisons, and regime scoring. |
evaluation
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macroforecast.evaluation
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Evaluation namespace exposing report functions plus metrics and tests; raw metric/test functions are not re-exported directly from it. |
pipeline
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macroforecast.pipeline
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Comprehensive POOS pipeline namespace: pipeline_spec, resolve_target, Arm, EvalSpec, CombinationContender, TCODE_TARGET_MAP. |
SearchSpec, SearchResult, SearchError, ParamDistribution, search_spec, select_params
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macroforecast.model_selection
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Model-parameter selection over a supplied window and metric. |
fixed, grid, random_search, cv_path, bayesian_search, genetic_search, custom_search, choice, uniform, log_uniform, randint
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macroforecast.model_selection
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Search specification and distribution builders. |
ForecastResult, run, run_forecast
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macroforecast.forecasting
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Windowed forecast runner. |
CombinationSpec, combination_spec, custom_combination, combine_mean, combine_median, combine_trimmed_mean, combine_winsorized_mean, combine_inverse_mspe, combine_dmspe, combine_best_n, combine_bates_granger, combine_granger_ramanathan, combine_constrained_ls, combine_eigenvector, combine_regularized, combine_linear_pool, combine_log_pool
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macroforecast.forecasting
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Runner-integrated and direct forecast combination methods. |