Mitigate Risks

Predictive Neural Networks

Sophisticated, predictive neural networks imitate brain functions to identify patterns in historical or new, incomplete data sets – letting you intelligently predict the future and make decisions to meet your goals. Lumivero's powerful, robust neural networks software enables you to optimize results, even in the face of uncertainty.

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Use Neural Networks to Intelligently Estimate the Future

Accurately predicting the future is critical for any business. The relationship between inputs and outputs can be very complex, and historical information is often the key to unlocking it. What can your data tell you about potential outcomes, and the key indicators you should influence? Predictive neural networks provide intelligent, pattern recognition analysis of your organization’s data using machine learning.

What are Predictive Neural Networks?

The Monte Carlo method is a computerized mathematical technique that allows people to quantitatively account for risk in forecasting and decision-making. At its core, the Monte Carlo method is a way to use random samples of parameters to explore the behavior of a complex system. A Monte Carlo simulation is used to handle an extensive range of problems in a variety of different fields to understand the impact of risk and uncertainty.

Data Mining to Uncover Patterns

Predictive neural networks are a sophisticated data mining application that imitate the function of the brain to detect patterns in data sets. These mathematical models can detect the most subtle and complex relationships between your variables. This type of predictive modeling is used in energy & utilities, healthcare & pharmaceuticals, insurance & reinsurance, finance & banking, manufacturing & consumer goods, logistics & transportation, and other fields. Applications include:

Make Informed Predictions and Forecasts

Predictive neural networks produce forecasted values or categories for future observations – critical information for your business. The most important predictor variables are also highlighted, providing more invaluable information to assist decision-making.

Use cases are varied, and include:

Price prediction

Reserves estimation

Fraud detection

Credit advising

Load forecasting

Process modeling and control

Portfolio management

Financial planning

Machine diagnostics

Medical diagnosis and more

How do Predictive Neural Networks Work?

Predictive neural networks are conceptually a complex network of connected nodes that “learn” the structure of your data. Initially they analyze historical data to determine how to predict the known output values using the given predictor variables. After this training phase, the neural net enters a testing phase using new data to ensure it has adequate predictive power when faced with previously unseen information. Once the network has achieved a sufficiently small prediction error it is ready to accurately predict the future based on what it’s “learned!”

Prediction error is impossible to eliminate entirely, and is available from every phase of the process; categorical prediction also includes a likelihood of error for each category. This information highlights the reliability of your trained neural network. Key contributing inputs are also ranked to inform potential control or mitigating decisions.

A testing report assessing the accuracy of a newly-created neural network.

Neural Networks Software from Lumivero

Lumivero’s NeuralTools software makes powerful, robust neural networks available to any Excel user. Typically, neural networks are found in large, proprietary enterprise applications, but NeuralTools brings this technique to where most users work, minimizing learning curves and maximizing access. NeuralTools may also be used in conjunction with Lumivero’s Evolver and RISKOptimizer products, which add optimization and Monte Carlo simulation techniques to your models. This enables you to optimize allocation of resources in order to produce the most desirable predicted result, even under uncertain conditions.

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