Predictive Neural Networks

Make Predictions from Incomplete Data

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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?

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:

  • Price prediction
  • Reserves estimation
  • Fraud detection
  • Credit advising
  • Load forecasting
  • Process modeling and control
  • Portfolio management
  • Financial planning
  • Machine diagnostics
  • Medical diagnosis and more

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.

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.

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

Neural Networks Software from Palisade

Palisade’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 Palisade’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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