Description Usage Arguments Value Author(s) References See Also Examples
This function conducts a Seasonal Adjustment analysis using X-11 and X-13ARIMA-SEATS methods from monthly raster time series using "seasonal" and "x13binary" packages.
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rasterts |
Input raster time series as |
rastermask |
Either a |
method |
Character. Defines the method to be used for the Seasonal Adjustment analysis. Accepts argument 'x11' (default) or 'x13'. |
gapfill |
Character. Defines the algorithm to be used to interpolate pixels with incomplete temporal profiles.
Accepts argument supported as method in function |
cores |
Integer. Defines the number of CPU to be used for multicore processing. Default to "1" core for singlecore processing. |
only.statistics |
Logical. If TRUE returns only the statistics from seasonal, trend and remainder components. |
keep.original |
Logical. If TRUE returns the original raster time series values in the 'rts' slot of |
... |
Additional arguments to be passed through to function |
Object of class STDstack-class
containing the following components:
std | Seasonal Trend Decomposition method used | |
mask | Final raster mask of computed pixels as RasterLayer object |
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seasonal_amplitude | Amplitude of seasonal component (statistic) as RasterLayer object |
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seasonal_amplitude_stdev | Standard deviation computed from the amplitude of seasonal component (statistic) as RasterLayer object |
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trend_slope | Trend slope computed from trend component (yearly statistic) as RasterLayer object |
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residual_stdev | Standard deviation computed from the remainder component (statistics) as RasterLayer object |
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rts | Input raster time series as RasterBrickTS object (only returned if keep.original = TRUE ) |
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seasonality | Seasonal component as RasterBrickTS object |
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trend | Trend component as RasterBrickTS object |
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seasonaladjtrend | Seasonal adjusted trend component as RasterBrickTS object |
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remainder | Remainder component as RasterBrickTS object |
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Federico Filipponi
Shiskin, J., Young, A.H., Musgrave, J.C. (1967). The X-11 variant of the Census Method II seasonal adjustment program. Technical Paper No. 15, U.S. Department of Commerce, U. S. Census Bureau.
Dagum, E.B. (1978). Modelling, forecasting and seasonally adjusting economic time series with the X-11 ARIMA method. Journal of the Royal Statistical Society. Series D (The Statistician), 27(3/4), 203-216.
Comprehensive list of R examples from the X-13ARIMA-SEATS manual: manual
Official X-13ARIMA-SEATS manual: pdf
seas
, stlplus
, rtsa.seas
, rtsa.gapfill
, stl
, decompose
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ## Not run:
## create raster time series using the 'pacificSST' data from 'remote' package
require(remote)
data(pacificSST)
pacificSST[which(getValues(pacificSST == 0))] <- NA # set NA values
# subset input for faster processing
pacificSST_clip <- crop(pacificSST, extent(260, 290, -15, 15))
# create rts object
rasterts <- rts(pacificSST_clip, seq(as.Date('1982-01-15'), as.Date('2010-12-15'), 'months'))
## generate raster mask
raster_mask <- pacificSST_clip[[1]] # create raster mask
names(raster_mask) <- "mask"
values(raster_mask) <- 1 # set raster mask values
raster_mask[which(is.na(getValues(pacificSST_clip[[1]])))] <- 0 # set raster mask values
## compute Seasonal Adjustment analysis
# use 'x11' (X-11) method to compute only adjusted seasonal trend decomposition statistics
std_x11_stats <- rtsa.seas(rasterts, rastermask=raster_mask, method="x11", only.statistics=TRUE)
# use 'x11' (X-11) method
std_x11_result <- rtsa.seas(rasterts=rasterts, rastermask=raster_mask, method="x11")
# use 'x13' (X-13ARIMA-SEATS) method
std_x13_result <- rtsa.seas(rasterts=rasterts, rastermask=raster_mask, method="x13")
# use 'x11' (X-11) method with multiple cores support and returning the original raster values
std_x11_res <- rtsa.seas(rasterts=rasterts, rastermask=raster_mask, cores=2, keep.original=TRUE)
## End(Not run)
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