NeuroShell Predictor
The NeuroShell Predictor
contains stateoftheart algorithms that train extremely fast, enabling
you to effectively solve prediction, forecasting, and estimation problems in a
minimum amount of time without going through the tedious process of tweaking
neural network parameters. Designed to be extremely easy to use, this product contains
our most powerful neural networks. Reads and writes text files for compatibility with
many other programs.
The prediction algorithms are the crowning achievement of several years of
research. Gone are the days of dozens of parameters that must be artistically
set to create a good model without overfitting. Gone are the days of hiring a
neural network expert or a statistician to build your predictive models.
Two of the most commonly heard complaints about previous prediction systems,
aside from being too hard to use, are that they are too slow or that they do not
accurately tell you how important each of the variables is to the model. We've
taken care of those problems. That's why we have two training models from which
to choose:
1. The first training method, which we call the “neural method” is based on an
algorithm called Turboprop2, a variant of the famous Cascade Correlation
algorithm invented at Carnegie Mellon University by Scott Fahlman. TurboProp2
dynamically grows hidden neurons and trains very fast. TurboProp2 models are
built (trained) in a matter of seconds compared to hours for older neural
networks types.
2. The second method, the “genetic training method”, is a genetic algorithm
variation of the General regression neural network (GRNN) invented by Donald
Specht. It trains everything in an outofsample mode; it is essentially doing a
"oneholdout" technique, also called "jackknife" or "cross validation". If you
train using this method, you are essentially looking at the training set
outofsample. This method is therefore extremely effective when you do not
have many patterns on which to train. The genetic training method takes longer
to train as more patterns are added to the training set.
Both training methods provide an analysis of independent
variables (inputs) to help you determine which ones are most important in
your model.
The NeuroShell Predictor is so easy to use that it doesn't need a manual!
Instead, there is an "Instructor" that guides you through making the
predictive models. At every stage of the Instructor, our extensive help file
will give you all the information you need. When you have learned from the
Instructor, you can turn it off and work from the toolbar or menus. The
program does include an online, context sensitive reference manual that you
may print yourself or just browse from your computer.
Finally, for those who want to embed the resulting neural models into your
own programs, or to distribute the results, there is an optional
RunTime Server available. Predictor models may be distributed without
incurring royalties or other fees.
The NeuroShell Predictor
reads data exported from spreadsheets and displays it in a datagrid.
You can select contiguous or
random data rows for training and outofsample sets.
You can select inputs and the
desired output from the columns in your data file. You can also select either
the neural or genetic training method.
There are only a couple of
settings the neural method requires, unlike the older backpropagation algorithm
which required extensive “parameter tweaking”.
The genetic training method
offers three modern optimization techniques and a choice of optimization goals.
After training the neural network may
be applied to training data or outofsample data.
NeuroShell Predictor comes with
a 3D graphics add on that is an efficient way to do sensitivity analysis:
The graph helps examine inputs for a Forex
prediction model.
This graph analyzes the inputs
for a sales forecast.
