Core Module Documentation#
- class neuralprophet.components.seasonality.base.Seasonality(config, id_list, quantiles, num_seasonalities_modelled, num_seasonalities_modelled_dict, n_forecasts, device)#
- class neuralprophet.components.seasonality.fourier.FourierSeasonality(config, id_list, quantiles, num_seasonalities_modelled, num_seasonalities_modelled_dict, n_forecasts, device)#
- abstract compute_fourier(features, name, meta=None)#
Compute single seasonality component.
- Parameters
features (torch.Tensor, float) – Features related to seasonality component, dims: (batch, n_forecasts, n_features)
name (str) – Name of seasonality. for attribution to corresponding model weights.
meta (dict) –
- Metadata about the all the samples of the model input batch. Contains the following:
df_name(list, str), time series ID corresponding to each sample of the input batch.
- Returns
Forecast component of dims (batch, n_forecasts)
- Return type
torch.Tensor
- forward(s, meta)#
Compute all seasonality components.
- Parameters
s (torch.Tensor, float) – dict of named seasonalities (keys) with their features (values) dims of each dict value (batch, n_forecasts, n_features)
meta (dict) –
- Metadata about the all the samples of the model input batch. Contains the following:
df_name(list, str), time series ID corresponding to each sample of the input batch.
- Returns
Forecast component of dims (batch, n_forecasts)
- Return type
torch.Tensor
- class neuralprophet.components.seasonality.fourier.GlobalFourierSeasonality(config, id_list, quantiles, num_seasonalities_modelled, num_seasonalities_modelled_dict, n_forecasts, device)#
- compute_fourier(features, name, meta=None)#
Compute single seasonality component.
- Parameters
features (torch.Tensor, float) – Features related to seasonality component, dims: (batch, n_forecasts, n_features)
name (str) – Name of seasonality. for attribution to corresponding model weights.
meta (dict) –
- Metadata about the all the samples of the model input batch. Contains the following:
df_name(list, str), time series ID corresponding to each sample of the input batch.
- Returns
Forecast component of dims (batch, n_forecasts)
- Return type
torch.Tensor
- class neuralprophet.components.seasonality.fourier.GlocalFourierSeasonality(config, id_list, quantiles, num_seasonalities_modelled, num_seasonalities_modelled_dict, n_forecasts, device)#
- compute_fourier(features, name, meta=None)#
Compute single seasonality component.
- Parameters
features (torch.Tensor, float) – Features related to seasonality component, dims: (batch, n_forecasts, n_features)
name (str) – Name of seasonality. for attribution to corresponding model weights.
meta (dict) –
- Metadata about the all the samples of the model input batch. Contains the following:
df_name(list, str), time series ID corresponding to each sample of the input batch.
- Returns
Forecast component of dims (batch, n_forecasts)
- Return type
torch.Tensor
- class neuralprophet.components.seasonality.fourier.LocalFourierSeasonality(config, id_list, quantiles, num_seasonalities_modelled, num_seasonalities_modelled_dict, n_forecasts, device)#
- compute_fourier(features, name, meta=None)#
Compute single seasonality component.
- Parameters
features (torch.Tensor, float) – Features related to seasonality component, dims: (batch, n_forecasts, n_features)
name (str) – Name of seasonality. for attribution to corresponding model weights.
meta (dict) –
- Metadata about the all the samples of the model input batch. Contains the following:
df_name(list, str), time series ID corresponding to each sample of the input batch.
- Returns
Forecast component of dims (batch, n_forecasts)
- Return type
torch.Tensor