Time Series Forecasting with Recurrent Neural Networks (RNNs) – part 3 – day 57

Time Series Forecasting with Recurrent Neural Networks (RNNs): A Complete Guide Introduction Time series data is all around us: from stock prices and weather patterns to daily ridership on public transport systems. Accurately forecasting future values in a time series is a challenging task, but Recurrent Neural Networks (RNNs) have proven to be highly effective at this. In this article, we will explore how RNNs can be applied to time series forecasting, explain the key concepts behind them, and demonstrate how to clean, prepare, and visualize time series data before feeding it into an RNN. What Is a Recurrent Neural Network (RNN)? A Recurrent Neural Network (RNN) is a type of neural network specifically designed for sequential data, such as time series, where the order of inputs matters. Unlike traditional feed-forward neural networks, RNNs have loops that allow them to carry information from previous inputs to future inputs. This makes them highly suitable for tasks where temporal dependencies are critical, such as language modeling or time series forecasting. How RNNs Learn: Backpropagation Through Time (BPTT) Understanding BPTT In a traditional feed-forward neural network, backpropagation is used to calculate how much each weight contributes to the error at each layer. In…