Neural Networks Approach - Computational Intelligence in Time Series Forecasting - page 102

Yet, whatever ratio is selected, attention should be paid to ensuring that the
training data set is large enough to cover all the dominant characteristic features
required for reliable network training as a forecaster. The remaining data set can
then be used for testing the trained network on the data samples never used in the
training. For this reason, it is recommended that the non-training data set should be
large enough to enable building of not only the test data set but also the validation
data set to be used in the overall network evaluation.
3.5.2 Determination of Network Architecture
This is the core task in building the neural network structure optimally adapted to
the specific problem the network should optimally solve. In our case it would be
the optimal predictor or the optimal forecaster. This task, although being very
challenging, is also the most difficult to execute because it requires from the
designer much skill and practical experience. Since being a nontrivial task with a
multiplicity of possible solutions, there are opinions that this work is more a kind
of art than an expert's routine. The issues addressed in the following present the
activities to be carried out when developing the network architecture. They include
the
x determination of input nodes required
x determination of output nodes
x selection of number of hidden layers
x selection of hidden neurons
x determination of node interconnection pattern
x selection of activity function of neurons.
Determination of the required number of input nodes is a relatively easy task,
because it depends predominantly on the number of independent variables
presented in the data set prepared. As a rule, each independent variable should be
represented by its own input node. In the case of input data prepared for
forecasting, the number of input nodes is directly determined by the number of
lagged values to be used for forecasting of the next value
x ( t +1) = f [ x ( t ), x ( t- 1), x ( t- 2), … , x ( t-n )],
as represented in Figure 3.13.