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.Neural
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.0.0

Syntax

C#
[SerializableAttribute]
public class TimeSeriesClassFilter
Visual Basic (Declaration)
<SerializableAttribute> _
Public Class TimeSeriesClassFilter
Visual C++
[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:

z=|x[0]:x[1]:x[2]
            :\ldots:x[nLags-1]|,
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

x^T=\{1,2,3,4,5,6,7,8,9,10\},
iClass^T=\{1,1,1,1,1,1,1,1,1,1\},
and
lag^T=\{0,2\}
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.

z=\left[\begin{array}{cc}
            1  & 3   \\
            2  & 4   \\
            3  & 5   \\
            4  & 6   \\
            5  & 7   \\
            6  & 8   \\
            7  & 9   \\
            8  & 10  \\
            9  & NaN \\
            10 & NaN \\
            \end{array}\right]

On the other hand, if the data were organized into two classes with

iClass^T=\{1,1,1,1,1,2,2,2,2,2\},
then nClasses is 2, and z is still a 2 by 10 matrix, but with the following values:

            z=\left[\frac{\begin{array}{cc}
            1  & 3   \\
            2  & 4   \\
            3  & 5   \\
            4  & NaN \\
            5  & NaN \\
            \end{array}}{\begin{array}{cc}
            6  & 8   \\
            7  & 9   \\
            8  & 10  \\
            9  & NaN \\
            10 & NaN \\
            \end{array}}\right]
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.

Inheritance Hierarchy

System..::.Object
Imsl.DataMining.Neural..::.TimeSeriesClassFilter

See Also