Exceedance probability forecasting with an LSTM

This recipe describes creating a probabilistic deep learning model to tackle exceedance tasks with a multivariate time series.

Getting ready

We’ll continue our example with the solar radiation dataset. Here’s the data module that we defined in the previous recipe:

N_LAGS = 14
HORIZON = 7
mvtseries = pd.read_csv('assets/daily_multivariate_timeseries.csv',
        parse_dates=['datetime'],
        index_col='datetime')
datamodule = ExceedanceDataModule(data=mvtseries,
        batch_size=64, test_size=0.3)

Now, let’s see how to create a classifier using an LSTM neural network and PyTorch’s LightningModule.

How to do it…

We will set up a binary classification using PyTorch Lightning’s LightningModule. Here’s the constructor and...