What Are the Uses of Neural Networks for Prediction?

Neural networks are complex computational models that are often used for pattern recognition. Because neural networks are modeled on biological brain functions, they are able to “learn” and predict results. There are many practical uses of neural networks for prediction, including financial calculation, weather forecasting, and medical diagnosis.

Artificial neural networks for prediction are inspired by the human brain. In a biological brain, many small processing units called “neurons” are connected into a large network. Each individual processing area is relatively simple, but the entire network is able solve complex problems when every neuron works together. The connections between each small neuron can be reconfigured into new network patterns. This allows the brain to reorganize itself and “learn” new concepts.

Like a human brain, an artificial neural network contains many small processors and connections, which can be reconfigured. The concept of using artificial neurons was first described by scientists Walter Pitts and Warren McCulloch in 1943. This scientific work was soon expanded and publicized by the famous artificial intelligence pioneer Alan Turing, who wrote about artificial neural networks in a 1948 publication titled “Intelligent Machinery.”

Financial calculation is one of the most common uses of neural networks for prediction. Essentially, a neural network is used as a mathematical “filter” to predict an outcome based on available financial data. This feature is often used in stock market prediction software. In this application, a computer processes previous market trends. Once a pattern has been established, the neural network calculates whether a stock will rise or fall in the future.

Neural networks can also be used to determine the credit rating of an individual or company. As with stock prediction, pattern recognition is the key. A network can consider thousands of past credit recipients, and analyze their financial history. By finding past trends, neural networks for prediction can estimate which new applicants are likely to default on their credit. These individuals receive a high-risk credit rating based on prediction.

Similarly, neural networks can be used for weather forecasting. Many different environmental factors such as temperature and wind currents can be fed into the network. Using a forecasting model that is based on previous climate patterns, the neural network can determine the probable result of current weather conditions.

The use of neural networks for prediction can also help solve certain medical problems. The human body is very complex, and dozens or even hundreds of factors can combine to cause a medical condition. Neural networks are sometimes able to deduce the source of a symptom. In this application, an artificial network can find trends and patterns from previous patient records, and predict the most likely cause of an illness.