models¶

Models for timeseries forecasting.

Modules

`pytorch_forecasting.models.base_model`	Timeseries models share a number of common characteristics.
`pytorch_forecasting.models.baseline`	Baseline model.
`pytorch_forecasting.models.basic_rnn`	Basic RNN model with LSTM or GRU cells
`pytorch_forecasting.models.deepar`	DeepAR: Probabilistic forecasting with autoregressive recurrent networks which is the one of the most popular forecasting algorithms and is often used as a baseline
`pytorch_forecasting.models.mlp`	Simple models based on fully connected networks
`pytorch_forecasting.models.nbeats`	N-Beats model for timeseries forecasting without covariates.
`pytorch_forecasting.models.nn`
`pytorch_forecasting.models.rnn`	Simple recurrent model - either with LSTM or GRU cells.
`pytorch_forecasting.models.temporal_fusion_transformer`	The temporal fusion transformer is a powerful predictive model for forecasting timeseries

check_for_nonfinite base_model