> ## Documentation Index > Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt > Use this file to discover all available pages before exploring further. > HINT: Hierarchical Mixture Networks for coherent probabilistic forecasting. Combines neural architectures with reconciliation for hierarchical time series. # HINT The Hierarchical Mixture Networks (HINT) are a highly modular framework that combines SoTA neural forecast architectures with task-specialized mixture probability and advanced hierarchical reconciliation strategies. This powerful combination allows HINT to produce accurate and coherent probabilistic forecasts. HINT’s incorporates a `TemporalNorm` module into any neural forecast architecture, the module normalizes inputs into the network’s non-linearities operating range and recomposes its output’s scales through a global skip connection, improving accuracy and training robustness. HINT ensures the forecast coherence via bootstrap sample reconciliation that restores the aggregation constraints into its base samples. **References** * [Kin G. Olivares, David Luo, Cristian Challu, Stefania La Vattiata, Max Mergenthaler, Artur Dubrawski (2023). “HINT: Hierarchical Mixture Networks For Coherent Probabilistic Forecasting”. Neural Information Processing Systems, submitted. Working Paper version available at arxiv.](https://arxiv.org/abs/2305.07089) * [Kin G. Olivares, O. Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, Lee Dicker (2022).”Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures”. International Journal Forecasting, accepted paper available at arxiv.](https://arxiv.org/pdf/2110.13179.pdf) * [Kin G. Olivares, Federico Garza, David Luo, Cristian Challu, Max Mergenthaler, Souhaib Ben Taieb, Shanika Wickramasuriya, and Artur Dubrawski (2022). “HierarchicalForecast: A reference framework for hierarchical forecasting in python”. Journal of Machine Learning Research, submitted, abs/2207.03517, 2022b.](https://arxiv.org/abs/2207.03517) Figure 1. Hierarchical Mixture Networks (HINT). *Figure 1. Hierarchical Mixture Networks (HINT).* ## 1. HINT ### `HINT` ```python theme={null} HINT(h, S, model, reconciliation, alias=None) ``` HINT The Hierarchical Mixture Networks (HINT) are a highly modular framework that combines SoTA neural forecast architectures with a task-specialized mixture probability and advanced hierarchical reconciliation strategies. This powerful combination allows HINT to produce accurate and coherent probabilistic forecasts. HINT's incorporates a `TemporalNorm` module into any neural forecast architecture, the module normalizes inputs into the network's non-linearities operating range and recomposes its output's scales through a global skip connection, improving accuracy and training robustness. HINT ensures the forecast coherence via bootstrap sample reconciliation that restores the aggregation constraints into its base samples.
Available reconciliations * BottomUp * MinTraceOLS * MinTraceWLS
* Identity **Parameters:** | Name | Type | Description | Default | | - | - | - | - | | `h` | [int](#int) | Forecast horizon. | *required* | | `model` | NeuralForecast model | Instantiated model class from [architecture collection](./models.html). | *required* | | `S` | [ndarray](#numpy.ndarray) | Dumming matrix of size (`base`, `bottom`) see HierarchicalForecast's [aggregate method](../hierarchicalforecast/utils.html#aggregate). | *required* | | `reconciliation` | [str](#str) | HINT's reconciliation method from \['BottomUp', 'MinTraceOLS', 'MinTraceWLS']. | *required* | | `alias` | [str](#str) | Custom name of the model. | None | #### `HINT.fit` ```python theme={null} fit( dataset, val_size=0, test_size=0, random_seed=None, distributed_config=None ) ``` HINT.fit HINT trains on the entire hierarchical dataset, by minimizing a composite log likelihood objective. HINT framework integrates `TemporalNorm` into the neural forecast architecture for a scale-decoupled optimization that robustifies cross-learning the hierarchy's series scales. **Parameters:** | Name | Type | Description | Default | | - | - | - | - | | `dataset` | [TimeSeriesDataset](#TimeSeriesDataset) | NeuralForecast's `TimeSeriesDataset` see details [here](./tsdataset.html) | *required* | | `val_size` | [int](#int) | size of the validation set, (default 0). | 0 | | `test_size` | [int](#int) | size of the test set, (default 0). | 0 | | `random_seed` | [int](#int) | random seed for the prediction. | None | **Returns:** | Name | Type | Description | | - | - | - | | `self` | | A fitted base `NeuralForecast` model. | #### `HINT.predict` ```python theme={null} predict(dataset, step_size=1, random_seed=None, **data_module_kwargs) ``` HINT.predict After fitting a base model on the entire hierarchical dataset. HINT restores the hierarchical aggregation constraints using bootstrapped sample reconciliation. **Parameters:** | Name | Type | Description | Default | | - | - | - | - | | `dataset` | [TimeSeriesDataset](#TimeSeriesDataset) | NeuralForecast's `TimeSeriesDataset` see details [here](./tsdataset.html) | *required* | | `step_size` | [int](#int) | steps between sequential predictions, (default 1). | 1 | | `random_seed` | [int](#int) | random seed for the prediction. | None | | `**data_kwarg` | | additional parameters for the dataset module. | *required* | **Returns:** | Name | Type | Description | | - | - | - | | `y_hat` | | numpy predictions of the `NeuralForecast` model. | ### Usage Example In this example we will use HINT for the hierarchical forecast task, a multivariate regression problem with aggregation constraints. The aggregation constraints can be compactcly represented by the summing matrix $\mathbf{S}_{[i][b]}$, the Figure belows shows an example. In this example we will make coherent predictions for the TourismL dataset. Outline: 1. Import packages 2. Load hierarchical dataset 3. Fit and Predict HINT 4. Forecast Plot ```python theme={null} import matplotlib.pyplot as plt from neuralforecast.losses.pytorch import GMM, sCRPS from datasetsforecast.hierarchical import HierarchicalData # Auxiliary sorting def sort_df_hier(Y_df, S_df): # NeuralForecast core, sorts unique_id lexicographically # by default, this class matches S_df and Y_hat_df order. Y_df.unique_id = Y_df.unique_id.astype('category') Y_df.unique_id = Y_df.unique_id.cat.set_categories(S_df.index) Y_df = Y_df.sort_values(by=['unique_id', 'ds']) return Y_df # Load TourismSmall dataset horizon = 12 Y_df, S_df, tags = HierarchicalData.load('./data', 'TourismLarge') Y_df['ds'] = pd.to_datetime(Y_df['ds']) Y_df = sort_df_hier(Y_df, S_df) level = [80,90] # Instantiate HINT # BaseNetwork + Distribution + Reconciliation nhits = NHITS(h=horizon, input_size=24, loss=GMM(n_components=10, level=level), max_steps=2000, early_stop_patience_steps=10, val_check_steps=50, scaler_type='robust', learning_rate=1e-3, valid_loss=sCRPS(level=level)) model = HINT(h=horizon, S=S_df.values, model=nhits, reconciliation='BottomUp') # Fit and Predict nf = NeuralForecast(models=[model], freq='MS') Y_hat_df = nf.cross_validation(df=Y_df, val_size=12, n_windows=1) Y_hat_df = Y_hat_df.reset_index() ``` ```python theme={null} # Plot coherent probabilistic forecast unique_id = 'TotalAll' Y_plot_df = Y_df[Y_df.unique_id==unique_id] plot_df = Y_hat_df[Y_hat_df.unique_id==unique_id] plot_df = Y_plot_df.merge(plot_df, on=['ds', 'unique_id'], how='left') n_years = 5 plt.plot(plot_df['ds'][-12*n_years:], plot_df['y_x'][-12*n_years:], c='black', label='True') plt.plot(plot_df['ds'][-12*n_years:], plot_df['HINT'][-12*n_years:], c='purple', label='mean') plt.plot(plot_df['ds'][-12*n_years:], plot_df['HINT-median'][-12*n_years:], c='blue', label='median') plt.fill_between(x=plot_df['ds'][-12*n_years:], y1=plot_df['HINT-lo-90'][-12*n_years:].values, y2=plot_df['HINT-hi-90'][-12*n_years:].values, alpha=0.4, label='level 90') plt.legend() plt.grid() plt.plot() ``` ## 2. Reconciliation Methods ### `get_identity_P` ```python theme={null} get_identity_P(S) ``` ### `get_bottomup_P` ```python theme={null} get_bottomup_P(S) ``` BottomUp Reconciliation Matrix. Creates BottomUp hierarchical "projection" matrix is defined as: ```math theme={null} \mathbf{P}_{\text{BU}} = [\mathbf{0}_{\mathrm{[b],[a]}}\;|\;\mathbf{I}_{\mathrm{[b][b]}}] ``` **Parameters:** | Name | Type | Description | Default | | - | - | - | - | | `S` | [ndarray](#numpy.ndarray) | Summing matrix of size (`base`, `bottom`). | *required* | **Returns:** | Type | Description | | - | - | | np.ndarray: Reconciliation matrix of size (`bottom`, `base`). | |
References * [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). "Data aggregation and information loss". The American Economic Review, 58 , 773(787)](http://www.jstor.org/stable/1815532).
### `get_mintrace_ols_P` ```python theme={null} get_mintrace_ols_P(S) ``` MinTraceOLS Reconciliation Matrix. Creates MinTraceOLS reconciliation matrix as proposed by Wickramasuriya et al. ```math theme={null} \mathbf{P}_{\text{MinTraceOLS}}=\left(\mathbf{S}^{\intercal}\mathbf{S}\right)^{-1}\mathbf{S}^{\intercal} ``` **Parameters:** | Name | Type | Description | Default | | - | - | - | - | | `S` | [ndarray](#numpy.ndarray) | Summing matrix of size (`base`, `bottom`). | *required* | **Returns:** | Type | Description | | - | - | | np.ndarray: Reconciliation matrix of size (`bottom`, `base`). | |
References * [Wickramasuriya, S.L., Turlach, B.A. & Hyndman, R.J. (2020). "Optimal non-negative forecast reconciliation". Stat Comput 30, 1167-1182, https://doi.org/10.1007/s11222-020-09930-0](https://robjhyndman.com/publications/nnmint/).
### `get_mintrace_wls_P` ```python theme={null} get_mintrace_wls_P(S) ``` MinTraceOLS Reconciliation Matrix. Creates MinTraceOLS reconciliation matrix as proposed by Wickramasuriya et al. Depending on a weighted GLS estimator and an estimator of the covariance matrix of the coherency errors $\\mathbf{W}\_{h}$. ```math theme={null} \mathbf{W}_{h} = \mathrm{Diag}(\mathbf{S} \mathbb{1}_{[b]}) ``` ```math theme={null} \mathbf{P}_{\text{MinTraceWLS}}=\left(\mathbf{S}^{\intercal}\mathbf{W}_{h}\mathbf{S}\right)^{-1} \mathbf{S}^{\intercal}\mathbf{W}^{-1}_{h} ``` **Parameters:** | Name | Type | Description | Default | | - | - | - | - | | `S` | [ndarray](#numpy.ndarray) | Summing matrix of size (`base`, `bottom`). | *required* | **Returns:** | Type | Description | | - | - | | np.ndarray: Reconciliation matrix of size (`bottom`, `base`). | |
References * [Wickramasuriya, S.L., Turlach, B.A. & Hyndman, R.J. (2020). "Optimal non-negative forecast reconciliation". Stat Comput 30, 1167-1182, https://doi.org/10.1007/s11222-020-09930-0](https://robjhyndman.com/publications/nnmint/).
## Related topics - [Hierarchical Forecast | NeuralForecast](/neuralforecast/docs/tutorials/hierarchical_forecasting.html.md) - [Time Series Scaling](/neuralforecast/docs/capabilities/time_series_scaling.html.md) - [TemporalNorm](/neuralforecast/common.scalers.html.md) - [About NeuralForecast](/neuralforecast/docs/getting-started/introduction.html.md) - [Forecasting Models](/neuralforecast/docs/capabilities/overview.html.md)