public class TimeSeriesClassFilter extends Object implements Serializable
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:
x
, and nLags is the number of computed lags.
The lag associated with x(i) is equal to the value in lag[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 lag[]
. If
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.Constructor and Description 

TimeSeriesClassFilter(int nClasses)
Constructor for
TimeSeriesClassFilter . 
Modifier and Type  Method and Description 

double[][] 
computeLags(int[] lags,
int[] iClass,
double[] x)
Computes lags of an array sorted first by class designations and then
descending chronological order.

public TimeSeriesClassFilter(int nClasses)
TimeSeriesClassFilter
.nClasses
 An int
specifying the number of
nominal categories associated with the time series.public double[][] computeLags(int[] lags, int[] iClass, double[] x)
lags
 An int
array containing the requested lags.
Every lag must be nonnegative.iClass
 An int
array containing class number
associated with each element of x
, sorted in ascending order. The ith
element is equal to the class associated with the ith
element of x
.
iClass
and x
must be
the same length.x
 A double
array containing the time series data to be
lagged. This array is assumed to be sorted first by
class designations and then descending chronological
order, i.e., most recent observations appear first
within a class.double
matrix containing the lagged data.
The ith column of this array is the lagged
values of x
for a lag equal to lags[i]
.
The number of rows is equal to the length of x
.Copyright © 19702015 Rogue Wave Software
Built October 13 2015.