Model parameters very much depend on the dataset for which they are destined.
Pytorch Forecasting provides a .from_dataset() method for each model that takes a TimeSeriesDataSet and additional parameters that cannot directy derived from the dataset such as, e.g. learning_rate or hidden_size.
.from_dataset()
TimeSeriesDataSet
learning_rate
hidden_size
To tune models, optuna can be used. For example, tuning of the TemporalFusionTransformer is implemented by optimize_hyperparameters()
TemporalFusionTransformer
optimize_hyperparameters()
See the API documentation for further details on available models:
models
Models for timeseries forecasting.