gluonts.nursery.spliced_binned_pareto.training_functions module

gluonts.nursery.spliced_binned_pareto.training_functions.eval_on_series(distr_tcn: gluonts.nursery.spliced_binned_pareto.distr_tcn.DistributionalTCN, optimizer: torch.optim.adam.Adam, series_tensor: torch.Tensor, ts_len: int, context_length: int, is_train: bool = False, return_predictions: bool = False, lead_time: int = 1)

Parameters

• distr_tcn (DistributionalTCN) –

• otimizer (Optimizer containing parameters, learning rate, etc) –

• series_tensor (Time series) –

• ts_len (Length of time series) –

• context_length (Number of time steps to input) –

• is_train (True if time series is training set) –

• return_predictions (True if to return (loss, predictions), False if to return loss only) –

• lead_time (Number of time steps to predict ahead) –

gluonts.nursery.spliced_binned_pareto.training_functions.highlight_min(data, color='lightgreen')

Highlights the minimum in a Series or DataFrame.

gluonts.nursery.spliced_binned_pareto.training_functions.plot_prediction(val_ts_tensor: torch.Tensor, predictions: torch.Tensor, context_length: int, lead_time: int = 1, start: int = 0, end: int = 500, fig: Optional[matplotlib.figure.Figure] = None)

Parameters

• val_ts_tensor (Time series) –

• predictions (Prediction series) –

• context_length (Number of time steps to input) –

• lead_time (Number of time steps to predict ahead) –

• start (Index of time series at which to start plotting) –

• end (Index of time series at which to end plotting) –

gluonts.nursery.spliced_binned_pareto.training_functions.train_step_from_batch(ts_chunks: torch.Tensor, targets: torch.Tensor, distr_tcn: gluonts.nursery.spliced_binned_pareto.distr_tcn.DistributionalTCN, optimizer: torch.optim.adam.Adam)

Parameters

• ts_chunks (Mini-batch chunked from the time series) –

• targets (Corresponding chunk of target values) –

• distr_tcn (DistributionalTCN) –

• otimizer (Optimizer containing parameters, learning rate, etc) –