Add a SA-item to a SAProcessing
Arguments
- jsap
SAProcessing.
- name
name of the SA-item to be added.
- x
either a seasonal adjustment model (from
rjd3x13::x13()orrjd3tramoseats::tramoseats()), a SA-item object,"ts"object.- spec
specification to use when
xis a"ts"object.
Examples
dir <- tempdir()
# Raw series
y <- rjd3toolkit::ABS$X0.2.09.10.M
# Creating an empty workspace and SAProcessing
jws <- jws_new()
jsap1 <- jws_sap_new(jws, "sap1")
# Adding SA-item as estimation result
# Estimation with rjd313
add_sa_item(jsap1, name = "series_1", x = rjd3x13::x13(y))
# Estimation with rjd3tramoseats
add_sa_item(jsap1, name = "series_2", x = rjd3tramoseats::tramoseats(y))
# Adding SA-item as raw series + specification
add_sa_item(jsap1, name = "series_3", x = y, rjd3x13::x13_spec("RSA3"))
add_sa_item(jsap1, name = "series_4", x = y, rjd3tramoseats::tramoseats_spec("RSAFull"))
rws <- read_workspace(jws)
rws$processing$sap1$series_4
#> $ts
#> $name
#> [1] "series_4"
#>
#> $moniker
#> $source
#> [1] ""
#>
#> $id
#> [1] "6bab65fe-da76-47c8-8e61-3658707825d0"
#>
#> attr(,"class")
#> [1] "JD3_TSMONIKER"
#>
#> $metadata
#> NULL
#>
#> $data
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct
#> 1982 460.1 502.6 443.8 459.1 438.4 465.1 452.7
#> 1983 379.2 378.0 472.1 503.4 510.6 462.4 468.3 458.2 482.7 485.3
#> 1984 414.7 414.5 484.7 487.3 597.9 500.4 543.4 503.4 522.8 556.6
#> 1985 516.3 452.5 525.8 587.7 700.3 561.8 602.8 582.5 563.1 637.1
#> 1986 570.5 478.2 547.4 594.3 751.6 553.4 663.2 581.1 661.9 665.6
#> 1987 613.9 513.2 599.9 674.1 714.0 670.5 720.9 601.6 672.3 709.1
#> 1988 631.0 551.1 678.1 715.7 740.8 722.0 683.5 650.9 723.3 729.6
#> 1989 631.5 552.0 719.0 697.6 764.8 786.3 715.1 723.8 757.9 751.7
#> 1990 678.2 586.2 726.8 744.1 815.5 832.4 710.3 759.4 741.1 786.6
#> 1991 694.0 604.7 719.2 748.2 828.2 746.9 794.5 770.4 741.5 858.6
#> 1992 740.0 665.9 701.5 831.4 878.6 826.0 788.2 723.6 819.8 902.5
#> 1993 762.1 643.0 754.1 840.7 906.6 887.1 771.5 728.7 844.7 886.9
#> 1994 745.7 664.4 821.5 831.7 908.0 912.6 782.9 798.8 887.0 934.6
#> 1995 752.4 682.5 811.2 906.0 927.2 906.8 880.6 873.9 856.8 920.6
#> 1996 833.1 737.1 812.0 895.2 962.8 908.6 908.0 888.9 833.7 933.7
#> 1997 840.9 727.4 857.9 849.0 994.8 830.2 971.1 836.0 939.1 976.9
#> 1998 917.3 716.2 822.9 970.1 970.2 849.4 1042.3 869.9 939.4 1021.3
#> 1999 942.0 738.4 903.2 953.2 1011.2 894.4 1054.5 899.5 1002.3 1043.7
#> 2000 924.9 798.2 901.9 1024.7 1052.3 1165.5 859.3 1009.2 1054.6 1070.4
#> 2001 971.9 814.6 1017.5 1039.2 1123.5 1024.9 1100.8 963.0 1012.9 1132.0
#> 2002 1027.9 841.4 1043.9 1075.3 1190.9 1143.0 1075.7 1065.9 1060.1 1211.4
#> 2003 1099.3 900.5 1092.7 1222.4 1237.1 1237.9 1182.0 1101.2 1198.2 1316.1
#> 2004 1182.9 989.8 1131.4 1277.1 1280.3 1384.1 1305.9 1166.8 1317.9 1358.3
#> 2005 1246.3 1037.3 1300.8 1153.7 1264.2 1454.2 1290.1 1210.7 1277.8 1314.4
#> 2006 1193.7 1037.7 1204.5 1348.6 1267.6 1429.0 1412.0 1239.2 1219.1 1344.6
#> 2007 1267.3 1047.0 1331.6 1302.6 1365.1 1491.5 1462.3 1315.5 1353.3 1440.6
#> 2008 1397.8 1140.5 1351.7 1396.6 1421.1 1401.6 1582.3 1268.4 1383.3 1452.4
#> 2009 1451.0 1056.6 1386.9 1509.1 1519.4 1500.5 1570.7 1341.5 1399.9 1534.3
#> 2010 1469.1 1111.9 1379.9 1389.7 1427.2 1551.4 1581.0 1324.0 1422.0 1464.9
#> 2011 1412.6 1117.5 1321.6 1472.6 1408.9 1471.9 1532.5 1293.5 1345.7 1404.7
#> 2012 1362.4 1131.7 1349.2 1391.2 1456.9 1616.4 1423.4 1359.0 1367.8 1442.6
#> 2013 1397.4 1113.6 1397.3 1339.1 1441.9 1537.4 1390.6 1337.2 1359.4 1463.3
#> 2014 1451.0 1064.9 1293.2 1442.9 1411.8 1461.6 1501.6 1254.2 1356.4 1478.7
#> 2015 1471.2 1053.8 1367.2 1442.2 1428.7 1480.9 1540.9 1331.9 1400.1 1566.3
#> 2016 1519.2 1155.8 1451.5 1451.0 1449.7 1596.1 1468.3 1293.9 1393.5 1497.4
#> 2017 1428.5 1092.4 1370.3 1522.6 1452.4 1557.2 1445.5 1303.1
#> Nov Dec
#> 1982 522.9 889.3
#> 1983 568.7 963.7
#> 1984 623.2 1039.4
#> 1985 697.1 1187.5
#> 1986 700.9 1367.9
#> 1987 743.2 1460.1
#> 1988 870.3 1570.0
#> 1989 923.8 1569.4
#> 1990 931.5 1563.1
#> 1991 944.7 1600.3
#> 1992 968.6 1650.9
#> 1993 970.0 1710.5
#> 1994 1000.4 1817.5
#> 1995 1067.4 1857.2
#> 1996 1081.6 1837.6
#> 1997 1111.3 1879.1
#> 1998 1137.7 1975.7
#> 1999 1207.2 2069.6
#> 2000 1232.5 2177.5
#> 2001 1344.8 2269.5
#> 2002 1495.1 2338.6
#> 2003 1528.2 2424.2
#> 2004 1536.7 2500.8
#> 2005 1540.4 2536.0
#> 2006 1623.3 2611.1
#> 2007 1687.9 2747.0
#> 2008 1675.9 2886.1
#> 2009 1736.6 2795.1
#> 2010 1705.5 2752.4
#> 2011 1660.0 2730.5
#> 2012 1672.9 2753.3
#> 2013 1668.9 2725.5
#> 2014 1687.7 2756.9
#> 2015 1730.5 2913.6
#> 2016 1684.3 2850.4
#> 2017
#>
#> attr(,"class")
#> [1] "JD3_TS"
#>
#> $domainSpec
#> Specification
#>
#> Series
#> Serie span: ALL
#> Preliminary Check: Yes
#>
#> Estimate
#> Model span: ALL
#> Tolerance: 1e-07
#> Exact ML: No
#> Unit root limit: 0.96
#>
#> Transformation
#> Function: AUTO
#> AIC difference:
#> Adjust: NONE
#>
#> Regression
#> Calendar regressor: TD7
#> with Leap Year: Yes
#> AutoAdjust: FALSE
#> Test: TEST_NO
#>
#> Easter: INCLUDEEASTER
#>
#> Pre-specified outliers: 0
#> Ramps: No
#> User-defined variables: No
#>
#> Outliers
#> Is enabled: No
#>
#> ARIMA
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> 0 0
#>
#> Specification SEATS
#> Approximation mode: APP_LEGACY
#> MA unit root boundary: 0.95
#> Trend boundary: 0.5
#> Seasonal tolerance: 2
#> Seasonal boundary: 0.8
#> Method: ALG_BURMAN
#>
#> Benchmarking
#> Is enabled: No
#>
#>
#> $estimationSpec
#> Specification
#>
#> Series
#> Serie span: ALL
#> Preliminary Check: Yes
#>
#> Estimate
#> Model span: ALL
#> Tolerance: 1e-07
#> Exact ML: No
#> Unit root limit: 0.96
#>
#> Transformation
#> Function: AUTO
#> AIC difference:
#> Adjust: NONE
#>
#> Regression
#> Calendar regressor: TD7
#> with Leap Year: Yes
#> AutoAdjust: FALSE
#> Test: TEST_NO
#>
#> Easter: INCLUDEEASTER
#>
#> Pre-specified outliers: 0
#> Ramps: No
#> User-defined variables: No
#>
#> Outliers
#> Is enabled: No
#>
#> ARIMA
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> 0 0
#>
#> Specification SEATS
#> Approximation mode: APP_LEGACY
#> MA unit root boundary: 0.95
#> Trend boundary: 0.5
#> Seasonal tolerance: 2
#> Seasonal boundary: 0.8
#> Method: ALG_BURMAN
#>
#> Benchmarking
#> Is enabled: No
#>
#>
#> $pointSpec
#> Specification
#>
#> Series
#> Serie span: ALL
#> Preliminary Check: Yes
#>
#> Estimate
#> Model span: ALL
#> Tolerance: 1e-07
#> Exact ML: No
#> Unit root limit: 0.96
#>
#> Transformation
#> Function: LOG
#> AIC difference:
#> Adjust: NONE
#>
#> Regression
#> Calendar regressor: TD7
#> with Leap Year: Yes
#> AutoAdjust: FALSE
#> Test: TEST_NO
#>
#> Easter: INCLUDEEASTER
#>
#> Pre-specified outliers: 2
#> - AO (2000-06-01)
#> - AO (2000-07-01)
#> Ramps: No
#> User-defined variables: No
#>
#> Outliers
#> Is enabled: No
#>
#> ARIMA
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> -0.8278 -0.4255
#>
#> Specification SEATS
#> Approximation mode: APP_LEGACY
#> MA unit root boundary: 0.95
#> Trend boundary: 0.5
#> Seasonal tolerance: 2
#> Seasonal boundary: 0.8
#> Method: ALG_BURMAN
#>
#> Benchmarking
#> Is enabled: No
#>
#>
#> $results
#> Model: TRAMO-SEATS
#> Log-transformation: yes
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> -0.8278 -0.4255
#>
#> Regression model:
#> monday tuesday wednesday thursday friday
#> -0.0109446 0.0048940 0.0001761 0.0132928 -0.0024801
#> saturday lp easter AO (2000-06-01) AO (2000-07-01)
#> 0.0153509 0.0410667 0.0503888 0.1681662 -0.1972348
#>
#> For a more detailed output, use the 'summary()' function.
#>
# Writing the workspace
save_workspace(jws, file.path(dir, "workspace.xml"))