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*BottomUp Reconciliation Matrix. Creates BottomUp hierarchical “projection” matrix is defined as: Parameters:
S
: Summing matrix of size (base
, bottom
).P
: Reconciliation matrix of size (bottom
,
base
).*MinTraceOLS Reconciliation Matrix. Creates MinTraceOLS reconciliation matrix as proposed by Wickramasuriya et al. Parameters:
S
: Summing matrix of size (base
, bottom
).P
: Reconciliation matrix of size (bottom
,
base
).*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 . Parameters:
S
: Summing matrix of size (base
, bottom
).P
: Reconciliation matrix of size (bottom
,
base
).*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:h
: int, Forecast horizon. model
:
NeuralForecast model, instantiated model class from architecture
collection.S
: np.ndarray, dumming matrix of size (base
, bottom
) see
HierarchicalForecast’s aggregate
method.reconciliation
: str, HINT’s reconciliation method from [‘BottomUp’,
‘MinTraceOLS’, ‘MinTraceWLS’].alias
: str, optional, Custom name
of the model.*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
hierachy’s series scales.
Parameters:dataset
: NeuralForecast’s
TimeSeriesDataset
see details
hereval_size
: int, size of the validation set, (default 0).test_size
: int, size of the test set, (default 0).random_seed
:
int, random seed for the prediction.self
: A fitted base
NeuralForecast
model.*HINT.predict After fitting a base model on the entire hierarchical dataset. HINT restores the hierarchical aggregation constraints using bootstrapped sample reconciliation. Parameters:
dataset
: NeuralForecast’s
TimeSeriesDataset
see details
herestep_size
: int, steps between sequential predictions, (default 1).random_seed
: int, random seed for the prediction.**data_kwarg
:
additional parameters for the dataset module.y_hat
: numpy predictions of the
NeuralForecast
model.