time_series_class

time_series_class_filter

Converts time series data sorted within nominal classes in decreasing chronological order to a useful format for processing by a neural network.

Synopsis

#include <imsls.h>

float *imsls_f_time_series_class_filter (int n_patterns, int n_lags, int n_classes, inti_class[], floatx[], …, 0)

The type double function is imsls_d_time_series_class_filter.

Required Arguments

int n_patterns (Input)
Number of observations. The number of observations must be greater than n_lags.

int n_lags (Input)
The number of lags. The number of lags must be one or greater.

int n_classes (Input)
The number of classes associated with these data. The number of classes must be one or greater.

int i_class[] (Input)
An array of length n_patterns. The i-th element in i_class is equal to the class associated with the i-th element of x. The classes must be numbered from 1 to n_classes.

float x[] (Input)
A sorted array of length n_patterns. This array is assumed to be sorted first by class designations and then descending by chronological order, i.e., most recent observations appear first within a class.

Return Value

A pointer to an internally allocated array of size n_patterns by n_lags columns. If errors are encountered, then NULL is returned.

Synopsis with Optional Arguments

#include <imsls.h>

float *imsls_f_time_series_class_filter (int n_patterns, int n_lags, int n_classes, int i_class[], float x[],

IMSLS_LAGS, int lag[],

IMSLS_RETURN_USER, float z[],

The type double function is imsls_d_time_series_class_filter.

Optional Arguments

IMSLS_LAGS, int lag[] (Input)
An array of length n_lags. The i-th element in lag is equal to the lag requested for the i-th column of z. Every lag must be non-negative.

Default: lag[i]=i

IMSLS_RETURN_USER, float z[] (Output)
A user-supplied array of size n_patterns by n_lags. The i-th column contains the lagged values of x for a lag equal to the number of lags in lag[i].

Description

The function imsls_f_time_series_class_filter accepts a data array, x[], and returns a new data array, z[], containing n_lags columns, each containing a lagged version of x.

The output data array, z, can be represented symbolically as:

z = |x(0) : x(1) : x(2) : … : x(n_lags-1)|,

where x(i) is the i-th lagged column of the incoming data array, x. Notice that n_lags is the number of lags and not the maximum lag. The maximum number of lags is max_lag= n_lags-1, unless the optional input lag[] is given, the highest lag is max_lags. If n_lags =2 and the optional input lag[] is not given, then the output array contains the lags 0, 1.

Consider an example in which n_patterns=10, n_lags =2 and

If and

then, n_classes=1 and z would contain 2 columns and 10 rows:

Note that since lagT = [0,1], the first column of z is formed using a lag of zero and the second is formed using a lag of two. A zero lag corresponds to no lag, which is why the first column of z in this example is equal to the original data in x.

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

then 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 class, and the last five are the lagged columns for the second class.

Example

Suppose that the training data to the neural network is represented by the following data matrix consisting of a single nominal variable coded into two binary columns and a single time series variable: