Univariate forecasting with an LSTM
This recipe walks you through the process of building an LSTM neural network for forecasting with univariate time series.
Getting ready
As we saw in Chapter 2, LSTM networks, a variant of RNNs, have gained substantial attention for their performance on time series and sequence data. LSTM networks are particularly suited for this task because they can effectively capture long-term temporal dependencies in the input data due to their inherent memory cells.
This section will extend our univariate time series forecasting to LSTM networks using PyTorch. So, we continue with the objects created in the previous recipe (Univariate forecasting with a feedforward neural network).
How to do it…
We will use the same train and test sets from the previous section. For an LSTM, we must reshape the input data to 3D. As we explored in the previous chapter, the three dimensions of the input tensor to LSTMs represent the following aspects:
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