Converts time series data contained within nominal categories to a
lagged format for processing by a neural network. Lagging is done within
the nominal categories associated with the time series.
Namespace:
Imsl.DataMining.NeuralAssembly: ImslCS (in ImslCS.dll) Version: 6.5.0.0
Syntax
C# |
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[SerializableAttribute] public class TimeSeriesClassFilter |
Visual Basic (Declaration) |
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<SerializableAttribute> _ Public Class TimeSeriesClassFilter |
Visual C++ |
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[SerializableAttribute] public ref class TimeSeriesClassFilter |
Remarks
Class TimeSeriesClassFilter can be used with a data array, x to compute a new data array, z[,], containing lagged columns of x.
When using the method ComputeLags, the output array, z[,] of lagged columns, can be symbolically represented as:
where x[i] is a lagged column of the incoming data array x, and nLags is the number of computed lags. The lag associated with x[i] is equal to the value in lags[i], and lagging is done within the nominal categories given in iClass. This requires the time series data in x[] be sorted in time order within each category iClass.Consider an example in which the number of observations in x[] is 10. There are two lags requested in lags. If
and then, all the time series data fall into a single category, i.e. nClasses = 1, and z would contain 2 columns and 10 rows. The first column reproduces the values in x[] because lags[0] = 0, and the second column is the 2nd lag because lags[1] = 2.
On the other hand, if the data were organized into two classes with
then nClasses is 2, and z is still a 2 by 10 matrix, but with the following values: The first 5 rows of z are the lagged columns for the first category, and the last five are the lagged columns for the second category.