Neural Networks Approach - Computational Intelligence in Time Series Forecasting - page 100

x design of network training strategy , i.e. selection of training algorithm,
performance index, and the training monitoring approach
x overall evaluation of forecasting results using fresh observation data sets.

3.5.1 Data Preparation for Forecasting

Data used for analysis and forecasting of time series are generally collected by
observations or by measurements. In engineering, of major interest is the analysis
of data obtained by sampling of corresponding sensor signals and forecasting their
future behaviour. Therefore, our attention will be primarily focused on forecasting
of experimental data taken from sensing elements placed within the experimental
setups or within the plant automation devices. Here, depending on the nature of
signals provided by sensors, two main critical issues are:

x the number of data needed for representative characterization of the
observed signal in view of its linearity, stationarity, drift, etc .
x the sampling period required for recording the entire frequency spectrum of
the sampled signal, but that will still considerably limit the noise frequency
spectrum.
In practice, the preprocessing of acquired data, because of the presence of noise,
drift, and sensor inaccuracy, represents a trial-and-error procedure. In the
preprocessing phase it should also be made clear whether data filtering, smoothing,
etc . are needed, or whether mathematical transformation of data will facilitate the
learning process of the network within its training and / or reduce the network
training time.
Data normalization is a process of final data preparation for their direct use for
network training. It includes the normalization of preprocessed data from their
natural range to the network's operating range, so that the normalized data are
strictly shaped to meet the requirements of the network input layer and are adapted
to the nonlinearities of the neurons, so that their outputs should not cross the
saturation limits.
In practice, the simplest normalization

x

i

x

ni

x

max

and the linear normalization

xx

x

i min

ni

x x

max min

are most frequently used. Moreover, instead of linear normalization, nonlinear
scaling or logarithmic scaling of input signals is used to moderate the possible
nonlinearity problems during the network training. For instance, logarithmic
transformation can squeeze the scale in the region of large data values, and