TSPAC
A Package of Programs for Time-Series Segmentation
 
 
 
SG1CMA   TSPAC    A1
     - univariate series, common variance, automatic mode
SG1CAS   TSPAC    A1
     - univariate series, common variance, automatic mode,
       sparse transition probability matrix
SGPDTA   TSPAC    A1
     - multiple time-series, automatic mode
 
 
 
C                        VERSION 1.1   9-OCT-1982                      C
C        PROGRAM IMPAC:TSSGPDTA WAS DEVELOPED FROM IMPAC:IMSGPDTA.     C
C                                                                      C
C        THE "TSSGP" PROGRAMS ARE FOR SEGMENTING MULTIVARIATE          C
C    TIME SERIES.  (FOR UNIVARIATE TIME SERIES THE "TSSG1" PROGRAMS    C
C     MAY BE USED.)                                                    C
C        IMPAC  INCLUDES PROGRAMS USING DISTANCE IN THE METRIC OF THE  C
C    ESTIMATED COMMON COVARIANCE MATRIX AS WELL AS PROGRAMS USING      C
C    DIFFERENT COVARIANCE MATRICES, WITH ADJUSTMENT BY THE             C
C     DETERMINANTS, I.E., USING ESTIMATED LOG LIKELIHOOD FOR THE       C
C     GAUSSIAN MODEL WITH DIFFERENT COVARIANCE MATRICES.               C
C    COVARIANCE MATRICES.                                              C
C        THERE ARE PROGRAMS IN MANUAL MODE, IN WHICH THE NUMBER OF     C
C     CLASSES AND INITIAL MEANS ARE INPUT AND PROGRAMS IN AUTOMATIC    C
C     MODE, WHICH TRY A RANGE OF NUMBERS OF CLASSES, WITH AUTOMATIC    C
C     SETTING OF INITIAL MEANS.                                        C
C                                                                      C
C                                                                      C
C        PROGRAMMED BY:                                                C
C    DR. STANLEY L. SCLOVE                   312/996-2681              C
C    DEPARTMENT OF QUANTITATIVE METHODS      312/996-2676              C
C    COLLEGE OF BUSINESS ADMINISTRATION                                C
C    UNIVERSITY OF ILLINOIS AT CHICAGO                                 C
C    BOX 4348, CHICAGO, IL  60680                                      C
C                                                                      C
C        RESEARCH SUPPORTED IN PART BY:                                C
C                                                                      C
C    ONR CONTRACT N00014-80-C-0408, TASK N042-443                      C
C    ARO CONTRACT DAAG29-82-K-0155                                     C
C                                                                      C
C        RESTRICTIONS (CAN BE MODIFIED):                               C
C    N, SERIES LENGTH, AT MOST  999;                                   C
C    IP, NUMBER OF VARIABLES, AT MOST 20;                              C
C    K, NUMBER OF CLASSES, AT MOST 29;                                 C
C    ITER, MAXIMUM NUMBER OF ITERATIONS, 20.                           C
C                                                                      C
C        SUBROUTINE(S) CALLED:                                         C
C    MATEQ, WHICH CALLS MATDT                                          C
C                                                                      C
C                                                                      C
CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
C
C
C
      DIMENSION X( 999,20),SUM(29,20)
      DIMENSION D(29),ICLUS( 999)
C
      DIMENSION IOTA( 999),JOTA(29)
C
      DIMENSION TITLE(18)
      DIMENSION NG(29),XMEAN(29,20)
      DIMENSION FMT(18)
      DIMENSION SS(29,20,20),SSD(29,20,20)
      DIMENSION WGSS(20,20)
      DIMENSION VARHAT(20,20),WGMS(20,20)
      DIMENSION ICLSOL( 999)
      DIMENSION XMIN(20),XMAX(20)
C
C
C     NOTE THAT DATA MATRIX REQUIRES ABOUT 200 K.
C
      DIMENSION IV(20,20)
      DIMENSION P(20,20)
C
      DIMENSION A(20,20)
C
      DIMENSION ET(29)
      DIMENSION PG(29,20,20)
C
      DIMENSION NT(29,29),IRSUM(29),TP(29,29)
      DIMENSION PROB(29)
C
C
      DOUBLE PRECISION SS,SUM
      DOUBLE PRECISION WGSS,SSD
      DOUBLE PRECISION VARHAT
C
C
CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC