forecast_with_lag_features

Class Optimization
# ...

  def forecast_with_lag_features(self, test_loader, batch_size=1, n_features=1, n_steps=100):
        test_loader_iter = iter(test_loader)
        predictions = []

        *_, (X, y) = test_loader_iter

        y = y.to(device).detach().numpy()
        X = X.view([batch_size, -1, n_features]).to(device)
        X = torch.roll(X, shifts=1, dims=2)
        X[..., -1, 0] = y.item(0)

        with torch.no_grad():
            self.model.eval()
            for _ in range(n_steps):
                X = X.view([batch_size, -1, n_features]).to(device)
                yhat = self.model(X)
                yhat = yhat.to(device).detach().numpy()
                X = torch.roll(X, shifts=1, dims=2)
                X[..., -1, 0] = yhat.item(0)
                predictions.append(yhat.item(0))

        return predictions
# ...