STL
gts bucketizeThe STL
function applies a Seasonal Trend decomposition on a Geo Time Series™ or a LIST of Geo Time Series™, which is based on multiple calls of RLOWESS
.
These Geo Time Series™ are the seasonal part and the trend part of the input Geo Time Series™. Their classnames are suffixed with "_seasonal" and "_trend".
This function can only be applied to bucketized Geo Time Series™ of numeric type.
The MAP parameter must have at least the field PERIOD
. You can provide additional optional parameters:
Optional parameters
Global
ROBUST
if TRUE, set defaults of number of inner and outer loops to 1 and 15 instead of 2 and 0. Default is FALSEPRECISION
number of inner loops (a LONG). Default is 2 or 1ROBUSTNESS
number of outer loops (a LONG). Default is 0 or 15
Seasonal extract
BANDWIDTH_S
the bandwidth (a LONG) of the local regression. Default is 7DEGREE_S
the degree (a LONG) of the polynomial fit. Default is 2SPEED_S
the number of values interpolated instead of estimated. Default is BANDWIDTH_S/10
Low frequency filtering
BANDWIDTH_L
the bandwidth (a LONG) of the local regression. Default is nextOdd(PERIOD)DEGREE_L
the degree (a LONG) of the polynomial fit. Default is 1SPEED_L
the number of values interpolated instead of estimated. Default is BANDWIDTH_L/10
Trend extract
BANDWIDTH_T
the bandwidth (a LONG) of the local regression. Default is nextOdd(ceiling(1.5*PERIOD/(1-(1.5/BANDWIDTH_S))))DEGREE_T
the degree (a LONG) of the polynomial fit. Default is 1SPEED_T
the number of values interpolated instead of estimated. Default is BANDWIDTH_T/10
Post seasonal smoothing
BANDWIDTH_P
the bandwidth (a LONG) of the local regression. Default is 0 (i.e. no post smoothing)DEGREE_P
the degree (a LONG) of the polynomial fit. Default is 2SPEED_P
the number of values interpolated instead of estimated. Default is BANDWIDTH_P/10
Additional fields
BANDWIDT
value of all BANDWIDTH_X fields that are not setDEGREE
value of all DEGREE_X fields that are not setSPEED
value of all SPEED_X fields that are not set
REFERENCE
Cleveland, Robert B., et al. "STL: A seasonal-trend decomposition procedure based on loess." Journal of Official Statistics 6.1 (1990): 3-73.