In the context of financial forecasting, how can Recurrent Neural Networks (RNNs) be effectively utilized for sequence prediction in business intelligence applications?

Recurrent Neural Networks can be trained on past sales, revenue, or inventory data so that each new prediction takes the whole history into account, much like a student who remembers earlier lessons to solve a new problem. They work by feeding the sequence of past values into the network one step at a time, letting hidden states carry information forward and updating them as new data arrives. By minimizing the error between the predicted and actual values, the RNN learns which patterns in the sequence—such as seasonal spikes or promotional effects—are most predictive. For example, an RNN can be given monthly sales of a product over several years and learn to forecast the next quarter’s sales, automatically adjusting for holidays or marketing campaigns. This approach lets businesses anticipate demand, optimize inventory, and make data‑driven decisions with greater accuracy.