Summarises the incumbent current_model against the best model stored in an
auto_seasonal_analysis() result. The comparison is intentionally compact:
it reports key diagnostics, the switching decision from sa_should_switch(),
and aligned seasonally adjusted and seasonal-component series when they are
available.
Arguments
- res
Result from
auto_seasonal_analysis().- current_model
A fitted
seasobject to compare against.
Value
A list of class "seasight_sa_compare" with elements:
decision, summary, series, diagnostics, and table. The decision
uses the incumbent-relative rule documented in sa_should_switch().
Examples
# \donttest{
if (requireNamespace("seasonal", quietly = TRUE)) {
current_model <- seasonal::seas(AirPassengers)
res <- auto_seasonal_analysis(
y = AirPassengers,
current_model = current_model,
max_specs = 3
)
sa_compare(res, current_model)
}
#> Model used in SEATS is different: (1 1 2)(1 0 0)
#> Model used in SEATS is different: (1 1 2)(1 0 0)
#> Model used in SEATS is different: (1 1 1)(1 0 0)
#> seas(
#> x = structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432), tsp = c(1949, 1960.91666666667, 12), class = "ts"),
#> seats.noadmiss = "no",
#> transform.function = "log",
#> arima.model = "(0 1 0)(0 1 1)",
#> seats = "",
#> regression.variables = "easter[8]",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> seas(
#> x = structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432), tsp = c(1949, 1960.91666666667, 12), class = "ts"),
#> seats.noadmiss = "no",
#> transform.function = "log",
#> arima.model = "(0 1 0)(0 1 1)",
#> seats = "",
#> regression.variables = "easter[8]",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> seas(
#> x = structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432), tsp = c(1949, 1960.91666666667, 12), class = "ts"),
#> seats.noadmiss = "no",
#> transform.function = "log",
#> arima.model = "(1 1 1)(0 1 1)",
#> seats = "",
#> regression.variables = "easter[8]",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> seas(
#> x = structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432), tsp = c(1949, 1960.91666666667, 12), class = "ts"),
#> seats.noadmiss = "no",
#> transform.function = "log",
#> arima.model = "(1 1 1)(0 1 1)",
#> seats = "",
#> regression.variables = "easter[8]",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> seas(
#> x = structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432), tsp = c(1949, 1960.91666666667, 12), class = "ts"),
#> seats.noadmiss = "no",
#> transform.function = "log",
#> arima.model = "(0 1 1)(0 1 1)",
#> seats = "",
#> regression.variables = "easter[1]",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> seas(
#> x = structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432), tsp = c(1949, 1960.91666666667, 12), class = "ts"),
#> seats.noadmiss = "no",
#> transform.function = "log",
#> arima.model = "(0 1 1)(0 1 1)",
#> seats = "",
#> regression.variables = "easter[1]",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> Model used in SEATS is different: (0 1 1)(0 0 1)
#> Model used in SEATS is different: (0 1 1)(0 0 1)
#> seas(
#> x = AirPassengers,
#> regression.variables = c("td1coef", "easter[1]", "ao1951.May"),
#> arima.model = "(0 1 1)(0 1 1)",
#> regression.aictest = NULL,
#> outlier = NULL,
#> transform.function = "log"
#> )
#> seas(
#> x = structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432), tsp = c(1949, 1960.91666666667, 12), class = "ts"),
#> seats.noadmiss = "no",
#> transform.function = "log",
#> arima.model = "(1 1 1)(0 1 1)",
#> seats = "",
#> regression.variables = "easter[8]",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> Model used in SEATS is different: (0 1 1)(0 0 1)
#> $decision
#> [1] "KEEP_CURRENT_MODEL"
#>
#> $summary
#> # A tibble: 6 × 4
#> metric current best difference
#> <chr> <dbl> <dbl> <dbl>
#> 1 AICc 947. 986. 39.1
#> 2 QS_p 1 1 0
#> 3 LB_p 0.225 0.126 -0.0984
#> 4 SA_L1_distance NA NA 1.80
#> 5 seasonal_RMS_distance NA NA 0.00453
#> 6 seasonal_correlation NA NA 0.999
#>
#> $series
#> $series$current_sa
#> Jan Feb Mar Apr May Jun Jul Aug
#> 1949 122.7133 124.7657 125.0734 127.5286 127.3584 126.1729 125.1457 126.7024
#> 1950 126.9652 133.9356 134.0148 132.9366 133.2807 139.1178 142.9399 145.0604
#> 1951 160.0289 160.6949 165.2095 165.3571 182.2769 163.9040 168.6223 169.8626
#> 1952 188.1778 190.2422 182.6815 182.7733 189.9504 199.0013 195.4762 201.6016
#> 1953 214.9681 219.4743 227.0110 235.6320 231.6708 221.9571 221.1423 224.8974
#> 1954 222.8428 215.8288 232.1046 229.5814 235.9381 238.6981 245.3815 242.5199
#> 1955 263.2717 269.4386 267.8543 271.6152 274.9857 282.5125 288.8002 287.3755
#> 1956 311.6350 312.2992 313.8468 323.5312 326.4293 329.7704 327.8913 331.2443
#> 1957 349.8119 353.0661 359.7925 360.1808 364.7847 367.1797 369.2604 372.0201
#> 1958 378.0331 375.5824 370.9383 362.8547 369.7736 382.4193 386.5488 393.5585
#> 1959 397.5069 406.2698 416.8007 417.8449 420.8383 419.4645 429.9997 433.9810
#> 1960 456.2693 452.9980 447.8696 471.4062 475.5142 475.0558 477.3729 479.9277
#> Sep Oct Nov Dec
#> 1949 128.8169 129.1127 131.6409 130.4162
#> 1950 148.4848 145.6924 144.5891 153.0516
#> 1951 172.4565 178.2798 183.9488 181.2071
#> 1952 200.7790 209.6572 212.0677 216.6910
#> 1953 226.8532 229.2295 224.6545 225.5820
#> 1954 246.9733 246.9832 256.0337 257.4320
#> 1955 296.9456 295.7621 299.2314 310.4833
#> 1956 330.7961 336.1438 342.1775 340.5172
#> 1957 378.9032 378.3931 380.3348 378.8534
#> 1958 382.9308 390.9163 383.5082 385.2432
#> 1959 439.0853 437.8677 450.7845 461.3548
#> 1960 481.6406 489.6361 489.7574 487.5447
#>
#> $series$best_sa
#> Jan Feb Mar Apr May Jun Jul Aug
#> 1949 123.6751 125.0906 125.2155 127.6106 125.9116 125.7775 125.5271 126.2590
#> 1950 127.0075 134.1361 134.0178 134.2432 129.7224 138.5917 143.7705 144.5163
#> 1951 159.2697 160.7422 166.0605 166.2053 176.4625 164.6902 168.3857 168.7835
#> 1952 187.3004 195.5300 185.8147 181.4503 186.9192 200.1274 193.4788 203.6306
#> 1953 215.2517 217.9166 226.3541 237.0984 231.9480 221.1531 219.1966 226.8334
#> 1954 224.4788 213.6758 231.4145 228.1743 237.0797 237.9297 245.9410 242.4771
#> 1955 265.4818 266.6554 266.5329 272.2938 274.4284 281.4066 292.4309 284.9555
#> 1956 311.7541 318.8236 313.0767 325.7651 323.5159 331.3564 329.0379 328.6457
#> 1957 346.9934 349.4220 363.4515 357.0041 361.4796 372.1442 367.5485 372.8872
#> 1958 374.8970 371.7644 371.2707 362.2371 369.3191 383.8620 385.2195 398.3052
#> 1959 397.2214 401.0308 414.9768 416.2682 424.6514 417.6186 428.0799 439.4040
#> 1960 459.4091 459.3195 442.9033 472.6701 475.4237 473.6735 484.1583 476.9264
#> Sep Oct Nov Dec
#> 1949 128.3384 130.1929 131.3651 130.1286
#> 1950 149.2877 145.8199 144.2294 154.4833
#> 1951 174.9373 177.2446 183.4845 183.3736
#> 1952 199.5114 208.3530 215.3070 215.2202
#> 1953 225.7786 229.5367 225.5594 224.2073
#> 1954 245.9918 249.1727 254.7634 255.8943
#> 1955 295.4728 298.4816 297.8414 311.2767
#> 1956 335.3918 333.2777 340.1982 344.1812
#> 1957 380.7451 376.2439 381.4098 379.4499
#> 1958 381.6646 388.4877 387.9094 382.6538
#> 1959 438.4094 438.9866 452.4871 459.4932
#> 1960 481.6272 495.5024 487.9812 490.8705
#>
#> $series$current_seasonal
#> Jan Feb Mar Apr May Jun Jul
#> 1949 0.9019909 0.9542175 1.0698054 1.0023238 0.9486743 1.0762915 1.1687498
#> 1950 0.9044253 0.9491504 1.0665026 0.9959364 0.9462061 1.0773716 1.1753610
#> 1951 0.9141398 0.9417802 1.0618930 0.9942760 0.9520064 1.0812071 1.1784129
#> 1952 0.9167921 0.9244155 1.0490375 0.9914602 0.9619895 1.0906339 1.1870718
#> 1953 0.9104194 0.9010165 1.0429827 0.9984889 0.9768772 1.1012838 1.2044124
#> 1954 0.9047060 0.8788380 1.0263130 0.9899166 0.9801523 1.1125437 1.2289230
#> 1955 0.9084206 0.8724821 1.0104347 0.9813505 0.9804222 1.1215922 1.2456033
#> 1956 0.9099794 0.8665834 0.9954911 0.9758214 0.9828361 1.1291156 1.2577076
#> 1957 0.9084879 0.8601435 0.9824858 0.9673156 0.9818267 1.1324750 1.2704668
#> 1958 0.9073863 0.8542446 0.9690255 0.9601876 0.9802351 1.1324730 1.2815049
#> 1959 0.9043100 0.8493213 0.9600489 0.9658416 0.9863019 1.1319021 1.2857469
#> 1960 0.9032140 0.8432997 0.9483270 0.9690174 0.9911469 1.1328470 1.2876816
#> Aug Sep Oct Nov Dec
#> 1949 1.1784737 1.0620085 0.9108650 0.7947027 0.9034620
#> 1950 1.1823424 1.0593844 0.9115370 0.7931061 0.9039952
#> 1951 1.1819479 1.0513155 0.9167609 0.7983953 0.9053339
#> 1952 1.1863075 1.0471047 0.9191085 0.7991875 0.9032417
#> 1953 1.1952541 1.0509099 0.9191185 0.7976929 0.8989483
#> 1954 1.2063680 1.0549012 0.9163132 0.7975557 0.8974620
#> 1955 1.2182118 1.0569141 0.9155538 0.7967154 0.8940588
#> 1956 1.2335300 1.0574570 0.9184159 0.7966728 0.8880924
#> 1957 1.2534584 1.0615283 0.9251866 0.7983848 0.8855795
#> 1958 1.2681131 1.0612632 0.9265177 0.7964929 0.8825474
#> 1959 1.2729665 1.0607040 0.9281347 0.7994991 0.8856520
#> 1960 1.2739133 1.0609690 0.9304721 0.8010242 0.8847667
#>
#> $series$best_seasonal
#> Jan Feb Mar Apr May Jun Jul
#> 1949 0.9055987 0.9433166 1.0632503 1.0022663 0.9609919 1.0733239 1.1790284
#> 1950 0.9054586 0.9393445 1.0611492 0.9970609 0.9635961 1.0751007 1.1824401
#> 1951 0.9104053 0.9331713 1.0571482 0.9943987 0.9747112 1.0808174 1.1818109
#> 1952 0.9129719 0.9205751 1.0476035 0.9890109 0.9790327 1.0893063 1.1887606
#> 1953 0.9105619 0.8994265 1.0398592 0.9937753 0.9872901 1.0987865 1.2043980
#> 1954 0.9087716 0.8798375 1.0242290 0.9863692 0.9870098 1.1095714 1.2279366
#> 1955 0.9115502 0.8737868 1.0103694 0.9794784 0.9838632 1.1193766 1.2447386
#> 1956 0.9109744 0.8688190 0.9985981 0.9742212 0.9829500 1.1286939 1.2551744
#> 1957 0.9077984 0.8614225 0.9879232 0.9664654 0.9820749 1.1339690 1.2651391
#> 1958 0.9069157 0.8553805 0.9751828 0.9605459 0.9828898 1.1332198 1.2745979
#> 1959 0.9062956 0.8528024 0.9649048 0.9645833 0.9890465 1.1302178 1.2801348
#> 1960 0.9076878 0.8512593 0.9541679 0.9669924 0.9927986 1.1294699 1.2847038
#> Aug Sep Oct Nov Dec
#> 1949 1.1721934 1.0596981 0.9140284 0.7916869 0.9067956
#> 1950 1.1763377 1.0583593 0.9120839 0.7904078 0.9062470
#> 1951 1.1790251 1.0518056 0.9139911 0.7957077 0.9052558
#> 1952 1.1884262 1.0475593 0.9167136 0.7988592 0.9014024
#> 1953 1.1991177 1.0497008 0.9192432 0.7980160 0.8964918
#> 1954 1.2083617 1.0528805 0.9190412 0.7968176 0.8949009
#> 1955 1.2177342 1.0559346 0.9179796 0.7957256 0.8930962
#> 1956 1.2323302 1.0584635 0.9181532 0.7965946 0.8890665
#> 1957 1.2523894 1.0610774 0.9222740 0.7996648 0.8854923
#> 1958 1.2678720 1.0585210 0.9240962 0.7991556 0.8806917
#> 1959 1.2721777 1.0560904 0.9271354 0.8000228 0.8814058
#> 1960 1.2706363 1.0547578 0.9303689 0.7992111 0.8800691
#>
#> $series$aligned_sa
#> $series$aligned_sa$prev
#> Jan Feb Mar Apr May Jun Jul Aug
#> 1949 122.7133 124.7657 125.0734 127.5286 127.3584 126.1729 125.1457 126.7024
#> 1950 126.9652 133.9356 134.0148 132.9366 133.2807 139.1178 142.9399 145.0604
#> 1951 160.0289 160.6949 165.2095 165.3571 182.2769 163.9040 168.6223 169.8626
#> 1952 188.1778 190.2422 182.6815 182.7733 189.9504 199.0013 195.4762 201.6016
#> 1953 214.9681 219.4743 227.0110 235.6320 231.6708 221.9571 221.1423 224.8974
#> 1954 222.8428 215.8288 232.1046 229.5814 235.9381 238.6981 245.3815 242.5199
#> 1955 263.2717 269.4386 267.8543 271.6152 274.9857 282.5125 288.8002 287.3755
#> 1956 311.6350 312.2992 313.8468 323.5312 326.4293 329.7704 327.8913 331.2443
#> 1957 349.8119 353.0661 359.7925 360.1808 364.7847 367.1797 369.2604 372.0201
#> 1958 378.0331 375.5824 370.9383 362.8547 369.7736 382.4193 386.5488 393.5585
#> 1959 397.5069 406.2698 416.8007 417.8449 420.8383 419.4645 429.9997 433.9810
#> 1960 456.2693 452.9980 447.8696 471.4062 475.5142 475.0558 477.3729 479.9277
#> Sep Oct Nov Dec
#> 1949 128.8169 129.1127 131.6409 130.4162
#> 1950 148.4848 145.6924 144.5891 153.0516
#> 1951 172.4565 178.2798 183.9488 181.2071
#> 1952 200.7790 209.6572 212.0677 216.6910
#> 1953 226.8532 229.2295 224.6545 225.5820
#> 1954 246.9733 246.9832 256.0337 257.4320
#> 1955 296.9456 295.7621 299.2314 310.4833
#> 1956 330.7961 336.1438 342.1775 340.5172
#> 1957 378.9032 378.3931 380.3348 378.8534
#> 1958 382.9308 390.9163 383.5082 385.2432
#> 1959 439.0853 437.8677 450.7845 461.3548
#> 1960 481.6406 489.6361 489.7574 487.5447
#>
#> $series$aligned_sa$new
#> Jan Feb Mar Apr May Jun Jul Aug
#> 1949 123.6751 125.0906 125.2155 127.6106 125.9116 125.7775 125.5271 126.2590
#> 1950 127.0075 134.1361 134.0178 134.2432 129.7224 138.5917 143.7705 144.5163
#> 1951 159.2697 160.7422 166.0605 166.2053 176.4625 164.6902 168.3857 168.7835
#> 1952 187.3004 195.5300 185.8147 181.4503 186.9192 200.1274 193.4788 203.6306
#> 1953 215.2517 217.9166 226.3541 237.0984 231.9480 221.1531 219.1966 226.8334
#> 1954 224.4788 213.6758 231.4145 228.1743 237.0797 237.9297 245.9410 242.4771
#> 1955 265.4818 266.6554 266.5329 272.2938 274.4284 281.4066 292.4309 284.9555
#> 1956 311.7541 318.8236 313.0767 325.7651 323.5159 331.3564 329.0379 328.6457
#> 1957 346.9934 349.4220 363.4515 357.0041 361.4796 372.1442 367.5485 372.8872
#> 1958 374.8970 371.7644 371.2707 362.2371 369.3191 383.8620 385.2195 398.3052
#> 1959 397.2214 401.0308 414.9768 416.2682 424.6514 417.6186 428.0799 439.4040
#> 1960 459.4091 459.3195 442.9033 472.6701 475.4237 473.6735 484.1583 476.9264
#> Sep Oct Nov Dec
#> 1949 128.3384 130.1929 131.3651 130.1286
#> 1950 149.2877 145.8199 144.2294 154.4833
#> 1951 174.9373 177.2446 183.4845 183.3736
#> 1952 199.5114 208.3530 215.3070 215.2202
#> 1953 225.7786 229.5367 225.5594 224.2073
#> 1954 245.9918 249.1727 254.7634 255.8943
#> 1955 295.4728 298.4816 297.8414 311.2767
#> 1956 335.3918 333.2777 340.1982 344.1812
#> 1957 380.7451 376.2439 381.4098 379.4499
#> 1958 381.6646 388.4877 387.9094 382.6538
#> 1959 438.4094 438.9866 452.4871 459.4932
#> 1960 481.6272 495.5024 487.9812 490.8705
#>
#> $series$aligned_sa$ok
#> [1] TRUE
#>
#> $series$aligned_sa$reason
#> [1] "ok"
#>
#>
#> $series$aligned_seasonal
#> $series$aligned_seasonal$prev
#> Jan Feb Mar Apr May Jun Jul
#> 1949 0.9019909 0.9542175 1.0698054 1.0023238 0.9486743 1.0762915 1.1687498
#> 1950 0.9044253 0.9491504 1.0665026 0.9959364 0.9462061 1.0773716 1.1753610
#> 1951 0.9141398 0.9417802 1.0618930 0.9942760 0.9520064 1.0812071 1.1784129
#> 1952 0.9167921 0.9244155 1.0490375 0.9914602 0.9619895 1.0906339 1.1870718
#> 1953 0.9104194 0.9010165 1.0429827 0.9984889 0.9768772 1.1012838 1.2044124
#> 1954 0.9047060 0.8788380 1.0263130 0.9899166 0.9801523 1.1125437 1.2289230
#> 1955 0.9084206 0.8724821 1.0104347 0.9813505 0.9804222 1.1215922 1.2456033
#> 1956 0.9099794 0.8665834 0.9954911 0.9758214 0.9828361 1.1291156 1.2577076
#> 1957 0.9084879 0.8601435 0.9824858 0.9673156 0.9818267 1.1324750 1.2704668
#> 1958 0.9073863 0.8542446 0.9690255 0.9601876 0.9802351 1.1324730 1.2815049
#> 1959 0.9043100 0.8493213 0.9600489 0.9658416 0.9863019 1.1319021 1.2857469
#> 1960 0.9032140 0.8432997 0.9483270 0.9690174 0.9911469 1.1328470 1.2876816
#> Aug Sep Oct Nov Dec
#> 1949 1.1784737 1.0620085 0.9108650 0.7947027 0.9034620
#> 1950 1.1823424 1.0593844 0.9115370 0.7931061 0.9039952
#> 1951 1.1819479 1.0513155 0.9167609 0.7983953 0.9053339
#> 1952 1.1863075 1.0471047 0.9191085 0.7991875 0.9032417
#> 1953 1.1952541 1.0509099 0.9191185 0.7976929 0.8989483
#> 1954 1.2063680 1.0549012 0.9163132 0.7975557 0.8974620
#> 1955 1.2182118 1.0569141 0.9155538 0.7967154 0.8940588
#> 1956 1.2335300 1.0574570 0.9184159 0.7966728 0.8880924
#> 1957 1.2534584 1.0615283 0.9251866 0.7983848 0.8855795
#> 1958 1.2681131 1.0612632 0.9265177 0.7964929 0.8825474
#> 1959 1.2729665 1.0607040 0.9281347 0.7994991 0.8856520
#> 1960 1.2739133 1.0609690 0.9304721 0.8010242 0.8847667
#>
#> $series$aligned_seasonal$new
#> Jan Feb Mar Apr May Jun Jul
#> 1949 0.9055987 0.9433166 1.0632503 1.0022663 0.9609919 1.0733239 1.1790284
#> 1950 0.9054586 0.9393445 1.0611492 0.9970609 0.9635961 1.0751007 1.1824401
#> 1951 0.9104053 0.9331713 1.0571482 0.9943987 0.9747112 1.0808174 1.1818109
#> 1952 0.9129719 0.9205751 1.0476035 0.9890109 0.9790327 1.0893063 1.1887606
#> 1953 0.9105619 0.8994265 1.0398592 0.9937753 0.9872901 1.0987865 1.2043980
#> 1954 0.9087716 0.8798375 1.0242290 0.9863692 0.9870098 1.1095714 1.2279366
#> 1955 0.9115502 0.8737868 1.0103694 0.9794784 0.9838632 1.1193766 1.2447386
#> 1956 0.9109744 0.8688190 0.9985981 0.9742212 0.9829500 1.1286939 1.2551744
#> 1957 0.9077984 0.8614225 0.9879232 0.9664654 0.9820749 1.1339690 1.2651391
#> 1958 0.9069157 0.8553805 0.9751828 0.9605459 0.9828898 1.1332198 1.2745979
#> 1959 0.9062956 0.8528024 0.9649048 0.9645833 0.9890465 1.1302178 1.2801348
#> 1960 0.9076878 0.8512593 0.9541679 0.9669924 0.9927986 1.1294699 1.2847038
#> Aug Sep Oct Nov Dec
#> 1949 1.1721934 1.0596981 0.9140284 0.7916869 0.9067956
#> 1950 1.1763377 1.0583593 0.9120839 0.7904078 0.9062470
#> 1951 1.1790251 1.0518056 0.9139911 0.7957077 0.9052558
#> 1952 1.1884262 1.0475593 0.9167136 0.7988592 0.9014024
#> 1953 1.1991177 1.0497008 0.9192432 0.7980160 0.8964918
#> 1954 1.2083617 1.0528805 0.9190412 0.7968176 0.8949009
#> 1955 1.2177342 1.0559346 0.9179796 0.7957256 0.8930962
#> 1956 1.2323302 1.0584635 0.9181532 0.7965946 0.8890665
#> 1957 1.2523894 1.0610774 0.9222740 0.7996648 0.8854923
#> 1958 1.2678720 1.0585210 0.9240962 0.7991556 0.8806917
#> 1959 1.2721777 1.0560904 0.9271354 0.8000228 0.8814058
#> 1960 1.2706363 1.0547578 0.9303689 0.7992111 0.8800691
#>
#> $series$aligned_seasonal$ok
#> [1] TRUE
#>
#> $series$aligned_seasonal$reason
#> [1] "ok"
#>
#>
#>
#> $diagnostics
#> $diagnostics$current
#> # A tibble: 1 × 7
#> model arima engine AICc QS_p LB_p transform
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 current (0 1 1)(0 1 1) seats 947. 1 0.225 log
#>
#> $diagnostics$best
#> # A tibble: 1 × 7
#> model arima engine AICc QS_p LB_p transform
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 best (1 1 1)(0 1 1) seats 986. 1 0.126 log
#>
#>
#> $table
#> # A tibble: 2 × 7
#> model arima engine AICc QS_p LB_p transform
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 current (0 1 1)(0 1 1) seats 947. 1 0.225 log
#> 2 best (1 1 1)(0 1 1) seats 986. 1 0.126 log
#>
#> attr(,"class")
#> [1] "seasight_sa_compare"
# }