A Theoretical Introduction to Recurrent Neural Networks: A Key Architecture for Time Series Data Analysis | by David Andres | October 2023

A picture from Andres Dallimonti we Unsplash

Are you interested in mastering time series forecasting or natural language processing? Then you should learn about recurrent neural networks.

Recurrent neural networks or RNNs are a specialized form of neural network architecture designed for sequential tasks. Unlike traditional feedforward neural networks, which treat each input as independent, RNNs excel in situations where sequence and context are critical.

The main difference from feedforward neural networks is the “self-loop” they have in their neurons. IN “composite” RNN diagram, you will typically see neurons or units connected in a sequence where each neuron receives an input and then passes its output (known as a hidden state) to the next neuron in the row. This is in contrast to traditional neural networks, where neurons only pass information in one direction: from the input layer to the output layer.

Schematic of the RNN model architecture and its composite equivalent. Image by author.

A “self-loop” in an RNN is represented by this hidden state that is carried from one step to the next in the sequence, helping it make better decisions based on what it knows from the past.

This self-loop allows the network to remember previous information and use it to inform the next steps, thus creating internal memory” which helps in sequence processing. This is crucial for tasks such as time series forecasting or natural language understanding where the order and context of the data points matter.

RNNs work on a straightforward but powerful principle: each neuron in the network not only processes the current input, but also includes “memory”—often referred to as “hidden state”—from previous steps. This hidden state allows the network to maintain contextual information over time, making RNNs ideal for tasks where understanding sequence and time is essential.

Let’s see how basic or vanilla RNN unit is made of:

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