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General
Where can I get additional information about neural networks?
A good introductory book for managers and business analysts is:
Bigus, J.P. (1996), Data Mining with Neural Networks: Solving Business Problemsfrom
Application Development to Decision Support, NY: McGrawHill.
For engineers and technicallyminded people we’d recommend to Start with: Fausett, L. (1994), Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Englewood Cliffs, NJ: Prentice Hall.
For financial specialists, bankers and traders we recommend Starting with: E. Michael Azoff (1994). Neural Network Time Series: Forecasting of Financial Markets NY: John Wiley and Sons, Inc.
How could I improve things to get better forecasting?
You have two ways to improve results:
1) improve you input data (for more information please read Preparing Data Sets
section in Advanced Issues chapter)
2) improve network topology selection and network training (for more information
please read Selecting Network Topology and Training Network sections in Advanced
Issues chapter).
When neural networks are a bad choice for my forecasting?
Neural networks cannot create or digest the information that is not contained
in your data. To properly train a neural network you need to have a lot of data.
You data should contain input parameters (signals, attributes, correlated values)
that affect the target value. Change of input parameters should lead to change of
target one.
So, if you have small amount of historical data or if you do not know, which parameters influence your target value, better use some other forecasting method.
In addition, there exist some problems that in principle cannot be solved by neural networks. Do not use neural networks (as well as other numerical methods) for problems like:
 predicting random or pseudorandom numbers, like lottery numbers
 forecasting cash flow, volumes of sales, etc. if your business isn’t stable and your market situation often changes dramatically.
 any problem where historical data have no use due to unbiased, rapid and significant changes in the problem environment.
Data Analysis and Preprocessing
How much historical data do I need?
You definitely need to have more records in the training subset than the total
number of input columns.
The number of records needed for training depends on the complexity of your problem
and amount of noise in your data. There are no exact rules. Typically, it’s recommended
to have at least 10 times as many records for training as input columns.
This may not be enough for problems with subtle and complex dependencies in data.
Try to add more data if your network has poor results.
What is a categorical column?
Each value of a categorical column represents a certain category. For example,
categorical is a column that contains only “Male” or “Female” as its values. Typically,
the number of different values in a categorical column is much less than the number
of records.
Categorical data should be encoded in a special way to be suitable for a neural
network.
You may manually mark a column as categorical in Expert Mode (using Details button
at Data Analysis Progress step). This feature may be beneficial for some cases.
For example, your data has a column “Model” that has values “1”, “2”, “3”. By default,
this column will be considered as a numeric, but it will be more beneficial to encode
it as a categorical one.
Why Forecaster XL ignored some rows and columns?
This may happen if some of your columns or rows are unsuitable for neural network.
For example, text or data/time data cannot be processed by neural network. Also,
some your rows may have missing or invalid data; such rows will be ignored.
To see which columns and rows were ignored look into Data Preprocessing Report.
Network Preparation
What is network training?
Network training means adjusting neural network weights. During training the
network analyzes the data you have provided and changes weights between network
units to reflect dependencies found in your data.
What training algorithm Forecaster XL uses?
Forecaster XL uses constructive algorithm to train network and select the network
topology. This constructive algorithm is developed by Research Group and is
capable of automatic selection and tuning of training parameters and network topology.
How Forecaster XL determines neural network topology suitable for
my problem?
See What training algorithm Forecaster XL uses above.
What stopping conditions should I specify to improve forecasting
quality?
As the first step we recommend you using default settings that means your network
is trained until error reduction is no longer possible. If forecasting error is
still unacceptably high we recommend reducing MSE value, reducing the error change
value and increasing number of iterations.
Why I cannot see MSE and absolute error in the network training
report?
When your target column is not numeric, it is hard to define unambiguously what
the absolute error is. For such cases it is better to use correct classification
rate to let you know what percentage of data was recognized correctly.
What is “error change” in stopping conditions?
Error change specifies the error change during several last iterations. This
parameter is useful for detection of situations when each new iteration has almost
no influence on error and thus the network cannot further improve its performance
and training should be stopped to save time.
Although one should be careful with this parameter because in certain cases the error can be decreased after a lot of “motionless” iterations. It's impossible to automatically detect such cases. We recommend setting 10 iterations, which is enough for most of problems. For certainty you can set up to 100 iterations.
How much time is required for network training?
The time required for network training depends on the number of inputs, number
of hidden units, amount of data, complexity of the task and capability of your computer.
Complete network training can continue from several seconds to several hours.
System Requirements: Windows 98 or above. Excel 2000 or above. Internet Explorer 5 or above
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