stl.doc

     PURPOSE
     STL decomposes a time series into seasonal and trend  components.
     It returns the components and robustness weights.
     SYNOPSIS
     stl(y, n, np, ns, nt, nl, isdeg, itdeg, ildeg, nsjump, ntjump,
             nljump, ni, no, rw, season, trend, work)
     integer n, np, ns, nt, nl, isdeg, itdeg, ildeg, nsjump, ntjump,
             nljump, ni, no
     real y(n), rw(n), season(n), trend(n), work(n+2*np,5)
     ARGUMENTS
     y       input, time series to be decomposed.
     n       input, number of values in y.
     np      input, the period of the seasonal component. For example,
             if  the  time series is monthly with a yearly cycle, then
             np=12.
     ns      input, length of the seasonal smoother.  The value of  ns
             should be an odd integer greater than or equal to 3; ns>6
             is recommended.   As  ns  increases  the  values  of  the
             seasonal component at a given point in the seasonal cycle
             (e.g., January values of a monthly series with  a  yearly
             cycle) become smoother.
     nt      input, length of the trend smoother.   The  value  of  nt
             should  be  an  odd integer greater than or equal to 3; a
             value of nt between 1.5*np and 2*np is  recommended.   As
             nt  increases  the  values  of the trend component become
             smoother.
     nl      input, length of the low-pass filter.  The  value  of  nl
             should  be an odd integer greater than or equal to 3; the
             smallest odd integer greater  than  or  equal  to  np  is
             recommended.
     isdeg   input, degree of locally-fitted  polynomial  in  seasonal
             smoothing.  The value is 0 or 1.
     itdeg   input,  degree  of  locally-fitted  polynomial  in  trend
             smoothing.  The value is 0 or 1.
     ildeg   input, degree of locally-fitted  polynomial  in  low-pass
             smoothing.  The value is 0 or 1.
     nsjump  input,  skipping  value  for  seasonal  smoothing.    The
             seasonal  smoother  skips  ahead  nsjump  points and then
             linearly interpolates in between.  The  value  of  nsjump
             should  be  a  positive  integer; if nsjump=1, a seasonal
             smooth is calculated  at  all  n  points.   To  make  the
             procedure  run  faster, a reasonable choice for nsjump is
             10%-20% of ns.
     ntjump  input, skipping value for trend smoothing.
     nljump  input, skipping value for the low-pass filter.
     ni      input, number of loops  for  updating  the  seasonal  and
             trend  components.   The value of ni should be a positive
             integer.  See the next argument for advice on the  choice
             of ni.
     no      input, number of iterations of robust fitting.  The value
             of  no  should be a nonnegative integer.  If the data are
             well behaved without outliers, then robustness iterations
             are not needed.  In this case set no=0, and set ni=2 to 5
             depending  on  how  much  security  you  want  that   the
             seasonal-trend   looping   converges.   If  outliers  are
             present then no=3 is  a  very  secure  value  unless  the
             outliers are radical, in which case no=5 or even 10 might
             be better.  If no>0 then set ni to 1 or 2.
     rw      output, final robustness weights. All rw are 1 if no=0.
     season  output, seasonal component.
     trend   output, trend component.
     work    workspace of (n+2*np)*5 locations.