Forecasting with an RNN using Keras

RNNs initially entered the spotlight with Natural Language Processing (NLP), as they were designed for sequential data, where past observations, such as words, have a strong influence on determining the next word in a sentence. This need for the artificial neural network to retain memory (hidden state) inspired the RNN architecture. Similarly, time series data is also sequential, and since past observations influence future observations, it also needs a network with memory. For example, an artificial neural network like the one in Figure 13.1 is considered a Feed-Forward Artificial Neural Network (FFN), as depicted by the arrows pointing from nodes in one layer to the next in one direction; each node has one input and one output. In RNNs, there is a feedback loop where the output of one node or neuron is fed back (the recursive part) as input, allowing the network to learn from a prior time step acting as a memory. Figure 13.3 shows a recurrent cell...