autots.models package

Submodules

autots.models.arch module

autots.models.base module

autots.models.basics module

autots.models.cassandra module

autots.models.composite module

autots.models.deepssm module

autots.models.dnn module

Neural Nets.

class autots.models.dnn.ElasticNetwork(size: int = 256, l1: float = 0.01, l2: float = 0.02, feature_subsample_rate: float | None = None, optimizer: str = 'adam', loss: str = 'mse', epochs: int = 20, batch_size: int = 32, activation: str = 'relu', verbose: int = 1, random_seed: int = 2024)

Bases: object

fit(X, y)
predict(X)
class autots.models.dnn.KerasRNN(rnn_type: str = 'LSTM', kernel_initializer: str = 'lecun_uniform', hidden_layer_sizes: tuple = (32, 32, 32), optimizer: str = 'adam', loss: str = 'huber', epochs: int = 50, batch_size: int = 32, shape=1, verbose: int = 1, random_seed: int = 2020)

Bases: object

Wrapper for Tensorflow Keras based RNN.

Parameters:

  • rnn_type (str) – Keras cell type ‘GRU’ or default ‘LSTM’

  • kernel_initializer (str) – passed to first keras LSTM or GRU layer

  • hidden_layer_sizes (tuple) – of len 1 or 3 passed to first keras LSTM or GRU layers

  • optimizer (str) – Passed to keras model.compile

  • loss (str) – Passed to keras model.compile

  • epochs (int) – Passed to keras model.fit

  • batch_size (int) – Passed to keras model.fit

  • verbose (int) – 0, 1 or 2. Passed to keras model.fit

  • random_seed (int) – passed to tf.random.set_seed()

fit(X, Y)

Train the model on dataframes of X and Y.

predict(X)

Predict on dataframe of X.

class autots.models.dnn.Transformer(head_size=256, num_heads=4, ff_dim=4, num_transformer_blocks=4, mlp_units=[128], mlp_dropout=0.4, dropout=0.25, optimizer: str = 'adam', loss: str = 'huber', epochs: int = 50, batch_size: int = 32, verbose: int = 1, random_seed: int = 2020)

Bases: object

Wrapper for Tensorflow Keras based Transformer.

based on: https://keras.io/examples/timeseries/timeseries_transformer_classification/

Parameters:

  • optimizer (str) – Passed to keras model.compile

  • loss (str) – Passed to keras model.compile

  • epochs (int) – Passed to keras model.fit

  • batch_size (int) – Passed to keras model.fit

  • verbose (int) – 0, 1 or 2. Passed to keras model.fit

  • random_seed (int) – passed to tf.random.set_seed()

fit(X, Y)

Train the model on dataframes of X and Y.

predict(X)

Predict on dataframe of X.

autots.models.dnn.transformer_build_model(input_shape, output_shape, head_size, num_heads, ff_dim, num_transformer_blocks, mlp_units, dropout=0, mlp_dropout=0)
autots.models.dnn.transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0)

autots.models.ensemble module

autots.models.gluonts module

autots.models.matrix_var module

autots.models.mlensemble module

autots.models.model_list module

Lists of models grouped by aspects.

autots.models.model_list.auto_model_list(n_jobs, n_series, frequency)
autots.models.model_list.model_list_to_dict(model_list)

Convert various possibilities to dict.

autots.models.neural_forecast module

autots.models.prophet module

autots.models.pytorch module

autots.models.sklearn module

autots.models.statsmodels module

autots.models.tide module

Module contents

Model Models