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.

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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.

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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

A Range of Outcomes (one col list)

Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows:

the extreme possibilities

the outcomes of going for broke and for the most conservative decision

along with all possible consequences for middle-of-the-road decisions

History of Monte Carlo Simulation

The technique was first used by scientists working on the atom bomb; it was named for Monte Carlo, the Monaco resort town renowned for its casinos. Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems.

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.

100% Microsoft Excel Integration

With PrecisionTree, you never leave your spreadsheet, allowing you to work in a familiar environment, and get up to speed quickly.

Full Statistics Reports and Graphs

See results in risk profile graphs, 2-way sensitivity, tornado graphs, spider graphs, policy suggestion reports, and strategy-region graphs.

Advanced Features

Set up your decision tree in Microsoft Excel exactly as you need it with logic nodes, reference nodes, linked trees, custom utility functions, and influence diagrams.

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Normal

Values are positively skewed, not symmetric like a normal distribution. It is used to represent values that don’t go below zero but have unlimited positive potential. Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.

Lognormal

Uniform

All values have an equal chance of occurring, and the user simply defines the minimum and maximum because they have no knowledge of which values are more likely than others. Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product.

Triangular

The user defines the minimum, most likely, and maximum values. Values around the most likely are more likely to occur. Variables that could be described by a triangular distribution include past sales history per unit of time and inventory levels.

PERT

The user defines the minimum, most likely, and maximum values, just like the triangular distribution. Values around the most likely are more likely to occur. However values between the most likely and extremes are more likely to occur than the triangular; that is, the extremes are not as emphasized. An example of the use of a PERT distribution is to describe the duration of a task in a project management model.

Discrete

FEATURES LIST (not always shown)

Feature	Benefit	Professional Edition	Industrial Edition
Optimization under uncertainty	Combines Monte Carlo simulation with sophisticated optimization techniques to find optimal solutions to uncertain problems. Used for budgeting, allocation, scheduling, and more.
Efficient Frontier Analysis	Especially useful in financial analysis, Efficient Frontiers determine the optimal return that can be expected from a portfolio at a given level of risk
Ranges for adjustable cells and constraints	Streamlined model setup and editing
Genetic algorithms	Find the best global solution while avoiding getting caught in local, “hill-climbing” solutions
Six solving methods, including GAs and OptQuest	Always have the best method for different types of problems
RISKOptimizer Watcher and Convergence Monitoring	Monitor progress toward best solutions in real time
Overlay of Optimized vs Original Distribution	Compare original output to optimized result to visually see improvements
Original, Best, Last model updating	Instantly see the effects of three solutions on your entire model

Random Sampling Versus Best Guess

During a Monte Carlo simulation, values are sampled at random from the input probability distributions. Each set of samples is called an iteration, and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen. It tells you not only what could happen, but how likely it is to happen.

Monte Carlo simulation provides a number of advantages over deterministic, or “single-point estimate” analysis:

Probabilistic Results. Results show not only what could happen, but how likely each outcome is.

Graphical Results. Because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.

Sensitivity Analysis. Deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results. This allows you to identify and mitigate factors which cause the most risk.

Scenario Analysis: In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis.

Correlation of Inputs. In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables. It’s important for accuracy to represent how, in reality, when some factors go up or down, others go up or down accordingly.

An enhancement to Monte Carlo simulation is the use of Latin Hypercube sampling, which samples more accurately from the full range of values within distribution functions and produces results more quickly.

Enterprise Licensing
Better Research, Insights, and Outcomes for All

Whether your organization’s focus is qualitative, quantitative, or mixed methods data analysis, we can help your whole team work better together — collaborating to aggregate, organize, analyze, and present your findings. Lumivero’s enterprise licensing options offer volume pricing for teams and organizations needing nine (9) or more licenses.

Enterprise licenses allow the flexibility to install Lumivero software and solutions on multiple computers (up to the maximum number of licenses that your site has purchased) with a centralized management solution.

Lumivero’s team-based solutions allow you to:

Stay up-to-date with free upgrades to the latest releases

Reduce IT costs with one platform deployed across your organization

Reassign licenses to different users as teams evolve

Centralize license and subscription management in one place

Streamline budget allocation, especially for smaller groups and consultancy firms

Enjoy a Dedicated Customer Success Manager and pro-rated rates for new users

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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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Predictive Neural Networks

Use Neural Networks to Intelligently Estimate the Future

What are Predictive Neural Networks?

Data Mining to Uncover Patterns

Make Informed Predictions and Forecasts

A Range of Outcomes (one col list)

History of Monte Carlo Simulation

How do Predictive Neural Networks Work?

100% Microsoft Excel Integration

Full Statistics Reports and Graphs

Advanced Features

Normal

Lognormal

Uniform

Triangular

PERT

Discrete

FEATURES LIST (not always shown)

Random Sampling Versus Best Guess

Enterprise LicensingBetter Research, Insights, and Outcomes for All

Lumivero’s team-based solutions allow you to:

Neural Networks Software from Lumivero

Enterprise Licensing
Better Research, Insights, and Outcomes for All