Forecasting with an RNN using PyTorch

In the previous recipes, you used Keras to build different deep learning architectures with minimal changes to code. This is one of the advantages of a high-level API – it allows you to explore and experiment with different architectures very easily.

In this recipe, you will build a simple RNN architecture using PyTorch, a low-level API.

Getting ready

You will be using the functions and steps used to prepare the time series for supervised learning. The one exception is with the features_target_ts function, it will be modified to return a PyTorch Tensor object as opposed to a NumPy ndarray object. In PyTorch, tensor is a data structure similar to NumPy's ndarray object but optimized to work with Graphical Processing Units (GPUs).

You can convert a NumPy ndarray to a PyTorch Tensor object using the torch.from_numpy() method and convert a PyTorch Tensor object to a NumPy ndarray object using the detach.numpy() method:

numpy_array...