GluonTS DeepAR Modeling Function (Bridge)
GluonTS DeepAR Modeling Function (Bridge)
deepar_fit_impl( x, y, freq, prediction_length, id, epochs = 5, batch_size = 32, num_batches_per_epoch = 50, learning_rate = 0.001, learning_rate_decay_factor = 0.5, patience = 10, minimum_learning_rate = 5e-05, clip_gradient = 10, weight_decay = 1e-08, init = "xavier", ctx = NULL, hybridize = TRUE, context_length = NULL, num_layers = 2, num_cells = 40, cell_type = "lstm", dropout_rate = 0.1, use_feat_dynamic_real = FALSE, use_feat_static_cat = FALSE, use_feat_static_real = FALSE, cardinality = NULL, embedding_dimension = NULL, distr_output = "default", scaling = TRUE, lags_seq = NULL, time_features = NULL, num_parallel_samples = 100 )
x |
A dataframe of xreg (exogenous regressors) |
y |
A numeric vector of values to fit |
freq |
A |
prediction_length |
Numeric value indicating the length of the prediction horizon |
id |
A quoted column name that tracks the GluonTS FieldName "item_id" |
epochs |
Number of epochs that the network will train (default: 5). |
batch_size |
Number of examples in each batch (default: 32). |
num_batches_per_epoch |
Number of batches at each epoch (default: 50). |
learning_rate |
Initial learning rate (default: 10-3 ). |
learning_rate_decay_factor |
Factor (between 0 and 1) by which to decrease the learning rate (default: 0.5). |
patience |
The patience to observe before reducing the learning rate, nonnegative integer (default: 10). |
minimum_learning_rate |
Lower bound for the learning rate (default: 5x10-5 ). |
clip_gradient |
Maximum value of gradient. The gradient is clipped if it is too large (default: 10). |
weight_decay |
The weight decay (or L2 regularization) coefficient. Modifies objective by adding a penalty for having large weights (default 10-8 ). |
init |
Initializer of the weights of the network (default: “xavier”). |
ctx |
The mxnet CPU/GPU context. Refer to using CPU/GPU in the mxnet documentation. (default: NULL, uses CPU) |
hybridize |
Increases efficiency by using symbolic programming. (default: TRUE) |
context_length |
Number of steps to unroll the RNN for before computing predictions (default: NULL, in which case context_length = prediction_length) |
num_layers |
Number of RNN layers (default: 2) |
num_cells |
Number of RNN cells for each layer (default: 40) |
cell_type |
Type of recurrent cells to use (available: 'lstm' or 'gru'; default: 'lstm') |
dropout_rate |
Dropout regularization parameter (default: 0.1) |
use_feat_dynamic_real |
Whether to use the 'feat_dynamic_real' field from the data (default: FALSE) |
use_feat_static_cat |
Whether to use the feat_static_cat field from the data (default: FALSE) |
use_feat_static_real |
Whether to use the feat_static_real field from the data (default: FALSE) |
cardinality |
Number of values of each categorical feature.
This must be set if |
embedding_dimension |
Dimension of the embeddings for categorical features (default: |
distr_output |
Distribution to use to evaluate observations and sample predictions (default: StudentTOutput()) |
scaling |
Whether to automatically scale the target values (default: TRUE) |
lags_seq |
Indices of the lagged target values to use as inputs of the RNN (default: NULL, in which case these are automatically determined based on freq) |
time_features |
Time features to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq) |
num_parallel_samples |
Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100) |
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