Index

B

backcast_fourier_order (autopycoin.layers.SeasonalityBlock property) backcast_periods (autopycoin.layers.SeasonalityBlock property) BaseBlock (class in autopycoin.layers) BaseLayer (class in autopycoin.layers)

BaseModel (class in autopycoin.models) batch_size (autopycoin.dataset.generator.WindowGenerator property) block_type (autopycoin.layers.BaseBlock property) blocks (autopycoin.models.Stack property)

C

check_valid_structure() (autopycoin.models.PoolNBEATS method) checks() (autopycoin.layers.BaseLayer method) (autopycoin.models.PoolNBEATS method) coefficient_factory() (autopycoin.layers.BaseBlock method) (autopycoin.layers.GenericBlock method) (autopycoin.layers.SeasonalityBlock method) (autopycoin.layers.TrendBlock method)

compile() (autopycoin.models.NBEATS method) (autopycoin.models.PoolNBEATS method) (autopycoin.models.QuantileModel method) create_generic_nbeats() (in module autopycoin.models) create_interpretable_nbeats() (in module autopycoin.models)

D

data (autopycoin.dataset.generator.WindowGenerator property)

date_columns (autopycoin.dataset.generator.WindowGenerator property) drop_rate (autopycoin.layers.BaseBlock property)

F

flat (autopycoin.dataset.generator.WindowGenerator property) forecast_fourier_order (autopycoin.layers.SeasonalityBlock property)

forecast_periods (autopycoin.layers.SeasonalityBlock property) from_array() (autopycoin.dataset.generator.WindowGenerator method)

G

g_backcast_neurons (autopycoin.layers.GenericBlock property) g_forecast_neurons (autopycoin.layers.GenericBlock property) GenericBlock (class in autopycoin.layers) get_additional_shapes() (autopycoin.layers.QuantileLayer method)

get_coefficients() (autopycoin.layers.BaseBlock method) (autopycoin.layers.GenericBlock method) (autopycoin.layers.SeasonalityBlock method) (autopycoin.layers.TrendBlock method)

H

handle_dim_in_losses_and_metrics() (autopycoin.models.BaseModel method)

has_quantiles (autopycoin.layers.QuantileLayer property)

I

init_params() (autopycoin.layers.BaseLayer method) (autopycoin.layers.UnivariateLayer method) input_columns (autopycoin.dataset.generator.WindowGenerator property) input_width (autopycoin.dataset.generator.WindowGenerator property) (autopycoin.layers.BaseBlock property) (autopycoin.models.NBEATS property) (autopycoin.models.Stack property)

is_g_trainable (autopycoin.layers.BaseBlock property) is_interpretable (autopycoin.layers.BaseBlock property) (autopycoin.models.NBEATS property) (autopycoin.models.Stack property)

K

known_columns (autopycoin.dataset.generator.WindowGenerator property)

L

label_columns (autopycoin.dataset.generator.WindowGenerator property) label_width (autopycoin.dataset.generator.WindowGenerator property) (autopycoin.layers.BaseBlock property) (autopycoin.models.NBEATS property) (autopycoin.models.Stack property)

losses_wrapper() (autopycoin.models.BaseModel method) (autopycoin.models.QuantileModel method)

M

metrics_wrapper() (autopycoin.models.BaseModel method) (autopycoin.models.QuantileModel method)

N

n_quantiles (autopycoin.layers.QuantileLayer property)

NBEATS (class in autopycoin.models) nbeats_type (autopycoin.models.NBEATS property)

P

p_degree (autopycoin.layers.TrendBlock property) PoolNBEATS (class in autopycoin.models) post_processing() (autopycoin.layers.BaseLayer method) (autopycoin.layers.QuantileLayer method) postprocessing_y() (autopycoin.models.PoolNBEATS method)

preprocessing() (autopycoin.layers.BaseLayer method) (autopycoin.layers.QuantileLayer method) (autopycoin.layers.UnivariateLayer method) preprocessing_x() (autopycoin.models.PoolNBEATS method) preprocessing_y() (autopycoin.models.PoolNBEATS method) production() (autopycoin.dataset.generator.WindowGenerator method)

Q

QuantileLayer (class in autopycoin.layers) QuantileLossError (class in autopycoin.losses)

QuantileModel (class in autopycoin.models) quantiles (autopycoin.layers.QuantileLayer property) QuantileTensor (class in autopycoin.extension_type)

R

random_ts() (in module autopycoin.data)

S

seasonality() (autopycoin.models.NBEATS method) SeasonalityBlock (class in autopycoin.layers) sequence_stride (autopycoin.dataset.generator.WindowGenerator property) shift (autopycoin.dataset.generator.WindowGenerator property) Spec (autopycoin.extension_type.QuantileTensor attribute) (autopycoin.extension_type.UnivariateTensor attribute)

Stack (class in autopycoin.models) stack_type (autopycoin.models.Stack property) stacks (autopycoin.models.NBEATS property) SymetricMeanAbsolutePercentageError (class in autopycoin.losses)

T

test (autopycoin.dataset.generator.WindowGenerator property) test_size (autopycoin.dataset.generator.WindowGenerator property)

train (autopycoin.dataset.generator.WindowGenerator property) trend() (autopycoin.models.NBEATS method) TrendBlock (class in autopycoin.layers)

U

UnivariateLayer (class in autopycoin.layers)

UnivariateModel() (in module autopycoin.models) UnivariateTensor (class in autopycoin.extension_type)

V

valid (autopycoin.dataset.generator.WindowGenerator property)

valid_size (autopycoin.dataset.generator.WindowGenerator property)

W

WindowGenerator (class in autopycoin.dataset.generator)