`gru` – Gated recurrent unit RNN (torch, optional).#

Operational op under axis family, sub-layer L4_A_model_selection, layer l4. Standalone callable: mf.functions.gru_fit.

Function signature#

mf.functions.gru_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
) -> GRUFitResult

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—

GRUFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.n_params`	`int`	Total number of trainable parameters in GRU + head.
`.n_features_in_`	`int`	Number of input features seen during fit.
`.hidden_size`	`int`	Width of the GRU hidden state.
`.epochs_used`	`int`	Number of training epochs completed.
`.final_loss`	`float`	Training MSE via no-grad forward pass after fitting.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Arch metadata table: model_type, hidden_size, n_features, n_params, epochs_used, final_loss.

Requires pip install macroforecast[deep]. Simpler than LSTM (one fewer gate); often comparable on macro panels.

When to use

Sequence-modelling baselines; LSTM ablations.

When NOT to use

Without [deep] installed.

Set params.family = "gru" in the relevant layer to activate this op within a recipe:

# Layer L4 recipe fragment
params:
  family: gru

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Cho et al. (2014) ‘Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation’, EMNLP.