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Time Series Analysis with Python Cookbook
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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.
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...