RBFForecaster

RBFForecaster

class RBFForecaster(window_length=10, hidden_size=32, batch_size=32, centers=None, gamma=1.0, rbf_type='gaussian', hidden_layers=[64, 32], optimizer='adam', lr=0.01, epochs=100, stride=1, criterion='mse', device='cpu', mode='ar', pred_len=None, activation='relu', dropout_rate=0.1)[source]

Forecasting model using RBF transformations and ‘NN’ layers for time series.

This forecaster uses an RBF layer to transform input time series data into a higher-dimensional space, which is then used by neural network layers for forecasting.

Parameters:

window_lengthint, optional (default=10)

Length of the input sequence for each sliding window.

hidden_sizeint, optional (default=32)

Number of units in the RBF layer.

batch_sizeint, optional (default=32)

Size of mini-batches for training.

centersarray-like, optional (default=None)

Center points for RBF transformations.

gammafloat, optional (default=1.0)

Scaling factor controlling the spread of the RBF.

rbf_typestr, optional (default=”gaussian”)

The type of RBF kernel to apply.

• "gaussian": \(\exp(-\gamma (t - c)^2)\)

• "multiquadric": \(\sqrt{1 + \gamma (t - c)^2}\)

• "inverse_multiquadric": \(\frac{1}{\sqrt{1 + \gamma (t - c)^2}}\)

hidden_layerslist of int, optional (default=[64, 32])

Sizes of linear layers following the RBF layer.

optimizer{“adam”, “sgd”, “rmsprop”}, optional (default=”adam”)

Type of optimizer to use.

lrfloat, optional (default=0.01)

Learning rate for optimizer.

epochsint, optional (default=100)

Number of training epochs.

strideint, optional (default=1)

Step size between windows.

criterionstr, optional (default=”mse”)

Loss function to use during training.

devicestr, optional (default=”cpu”)

Device to use for training and computation. Options are “cpu” or “cuda” for GPU computation if available.

mode{“ar”, “direct”}, optional (default=”ar”)

Forecasting mode:

• "ar": Autoregressive mode for one-step-ahead predictions.

• "direct": Direct mode for multi-step-ahead predictions.

pred_lenint, optional (default=None)

Prediction length, i.e., the number of future time steps to forecast. Defines the network output dimension in direct mode. In AR mode this is ignored (output is always 1). Required for pretraining in direct mode if fh is not passed to pretrain().

activationstr, optional (default=”relu”)

Activation function to apply after each linear layer. Supported values are: "relu", "leaky_relu", "elu", "selu", "tanh", "sigmoid", "gelu".

dropout_ratefloat, optional (default=0.1)

Dropout rate applied after each hidden layer. A value of 0 disables dropout.

Attributes:

cutoff

Cut-off = “present time” state of forecaster.

fh

Forecasting horizon that was passed.

is_fitted

Whether fit has been called.

state

State of the estimator.

Methods

build_pytorch_pred_dataloader(y, fh)	Build PyTorch DataLoader for prediction.
build_pytorch_train_dataloader(y)	Build PyTorch DataLoader for training.
check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(y[, X, fh])	Fit forecaster to training data.
fit_predict(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_pretrained_params([deep])	Get pretrained parameters of this estimator.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params()	Return testing parameter settings for the estimator.
get_y_true(y)	Get y_true values for validation.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
predict([fh, X])	Forecast time series at future horizon.
predict_interval([fh, X, coverage])	Compute/return prediction interval forecasts.
predict_proba([fh, X, marginal])	Compute/return fully probabilistic forecasts.
predict_quantiles([fh, X, alpha])	Compute/return quantile forecasts.
predict_residuals([y, X])	Return residuals of time series forecasts.
predict_var([fh, X, cov])	Compute/return variance forecasts.
pretrain(y[, X, fh])	Pre-train forecaster on panel (global) data.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
update(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
update_predict(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
update_predict_single([y, fh, X, update_params])	Update model with new data and make forecasts.