Models¶
-
class
models.
WaveNet
(input_channels, output_channels, horizon, hidden_channels=64, skip_channels=64, dense_units=128, n_layers=7, n_blocks=1, dilation=2)¶ Implements WaveNet architecture for time series forecasting. Inherits from pytorch Module. Vector forecasts are made via a fully-connected layer.
- References:
- Arguments:
- input_channels (int): Number of covariates in input time series.
- output_channels (int): Number of target time series.
- horizon (int): Number of time steps to forecast.
- hidden_channels (int): Number of channels in convolutional hidden layers.
- skip_channels (int): Number of channels in convolutional layers for skip connections.
- dense_units (int): Number of hidden units in final dense layer.
- n_layers (int): Number of layers per Wavenet block (determines receptive field size).
- n_blocks (int): Number of Wavenet blocks.
- dilation (int): Dilation factor for temporal convolution.
Inititalize variables.
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decode
(inputs: <sphinx.ext.autodoc.importer._MockObject object at 0x7f7e27095da0>)¶ Returns forecasts based on embedding vectors.
- Arguments:
- inputs: embedding vectors to generate forecasts for
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encode
(inputs: <sphinx.ext.autodoc.importer._MockObject object at 0x7f7e26fcd908>)¶ Returns embedding vectors.
- Arguments:
- inputs: time series input to make forecasts for
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forward
(inputs)¶ Forward function.
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n_parameters
¶ Returns the number of model parameters.
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receptive_field_size
¶ Returns the length of the receptive field.