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.
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 2)(1 0 0)
#> 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)",
#> regression.variables = "easter[15]",
#> seats = "",
#> regression.aictest = NULL,
#> outlier = NULL
#> )
#> $decision
#> [1] "CHANGE_TO_NEW_MODEL"
#>
#> $summary
#> # A tibble: 6 × 4
#> metric current best difference
#> <chr> <dbl> <dbl> <dbl>
#> 1 AICc 947. 987. 40.3
#> 2 QS_p 1 1 0
#> 3 LB_p 0.225 0.147 -0.0780
#> 4 SA_L1_distance NA NA 1.85
#> 5 seasonal_RMS_distance NA NA 0.00465
#> 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 123.7039 125.1438 125.6189 127.2713 125.9047 125.7578 125.5002 126.2259
#> 1950 127.0104 134.1897 133.1920 135.1101 129.6836 138.5674 143.7577 144.5018
#> 1951 159.2940 160.7881 166.8819 165.4502 176.4916 164.6686 168.3369 168.7443
#> 1952 187.3328 195.5907 185.5886 181.6608 186.9073 200.1135 193.4373 203.6043
#> 1953 215.2539 217.9163 226.1554 237.4259 231.9964 221.1382 219.1531 226.8165
#> 1954 224.4553 213.6011 232.0176 227.5803 237.1041 237.9385 245.9545 242.4599
#> 1955 265.4959 266.6425 265.0050 273.8459 274.4058 281.4297 292.4698 284.9349
#> 1956 311.7761 318.7927 314.2138 324.5742 323.5327 331.4021 329.0651 328.6310
#> 1957 346.9755 349.3797 363.9722 356.4556 361.4695 372.2019 367.5848 372.9350
#> 1958 374.8780 371.7228 369.7398 363.5852 369.2604 383.8987 385.2887 398.4095
#> 1959 397.1753 400.9680 416.0246 415.1639 424.6932 417.6227 428.1307 439.4921
#> 1960 459.3946 459.2463 442.9714 472.4483 475.5008 473.6975 484.2435 476.9768
#> Sep Oct Nov Dec
#> 1949 128.3286 130.1753 131.3453 130.1246
#> 1950 149.2902 145.7918 144.1919 154.4849
#> 1951 174.9166 177.2306 183.4834 183.3867
#> 1952 199.4804 208.3473 215.3254 215.2287
#> 1953 225.7667 229.5462 225.5631 224.1952
#> 1954 245.9748 249.1649 254.7539 255.8904
#> 1955 295.4729 298.4689 297.8255 311.2879
#> 1956 335.3923 333.2479 340.1784 344.1716
#> 1957 380.7826 376.2544 381.4321 379.4460
#> 1958 381.6716 388.4741 387.8868 382.5857
#> 1959 438.4003 438.9892 452.4980 459.4851
#> 1960 481.6046 495.5390 487.9608 490.8070
#>
#> $series$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 0.9053878 0.9429152 1.0615976 1.0032707 0.9610442 1.0734923 1.1792811
#> 1950 0.9054377 0.9389695 1.0592895 0.9985551 0.9638847 1.0752888 1.1825456
#> 1951 0.9102666 0.9329051 1.0556556 0.9954256 0.9745505 1.0809588 1.1821534
#> 1952 0.9128138 0.9202890 1.0463119 0.9902896 0.9790952 1.0893818 1.1890159
#> 1953 0.9105524 0.8994280 1.0384789 0.9945970 0.9870842 1.0988601 1.2046374
#> 1954 0.9088668 0.8801452 1.0232644 0.9873024 0.9869082 1.1095304 1.2278692
#> 1955 0.9115017 0.8738293 1.0095466 0.9803405 0.9839442 1.1192847 1.2445730
#> 1956 0.9109102 0.8689033 0.9984938 0.9743589 0.9828991 1.1285384 1.2550709
#> 1957 0.9078450 0.8615268 0.9881498 0.9663465 0.9821023 1.1337933 1.2650143
#> 1958 0.9069617 0.8554763 0.9756640 0.9604730 0.9830462 1.1331113 1.2743692
#> 1959 0.9064008 0.8529358 0.9658695 0.9637495 0.9889492 1.1302069 1.2799828
#> 1960 0.9077164 0.8513950 0.9556070 0.9658411 0.9926377 1.1294127 1.2844778
#> Aug Sep Oct Nov Dec
#> 1949 1.1725012 1.0597792 0.9141518 0.7918060 0.9068231
#> 1950 1.1764562 1.0583411 0.9122601 0.7906132 0.9062372
#> 1951 1.1792988 1.0519299 0.9140633 0.7957124 0.9051912
#> 1952 1.1885798 1.0477221 0.9167384 0.7987911 0.9013666
#> 1953 1.1992072 1.0497561 0.9192050 0.7980028 0.8965402
#> 1954 1.2084471 1.0529533 0.9190701 0.7968473 0.8949145
#> 1955 1.2178220 1.0559346 0.9180185 0.7957679 0.8930641
#> 1956 1.2323854 1.0584620 0.9182352 0.7966409 0.8890914
#> 1957 1.2522288 1.0609727 0.9222484 0.7996181 0.8855016
#> 1958 1.2675402 1.0585017 0.9241285 0.7992022 0.8808484
#> 1959 1.2719227 1.0561125 0.9271300 0.8000036 0.8814214
#> 1960 1.2705020 1.0548072 0.9303001 0.7992446 0.8801831
#>
#> $series$aligned_sa
#> $series$aligned_sa$prev
#> [1] 122.7133 124.7657 125.0734 127.5286 127.3584 126.1729 125.1457 126.7024
#> [9] 128.8169 129.1127 131.6409 130.4162 126.9652 133.9356 134.0148 132.9366
#> [17] 133.2807 139.1178 142.9399 145.0604 148.4848 145.6924 144.5891 153.0516
#> [25] 160.0289 160.6949 165.2095 165.3571 182.2769 163.9040 168.6223 169.8626
#> [33] 172.4565 178.2798 183.9488 181.2071 188.1778 190.2422 182.6815 182.7733
#> [41] 189.9504 199.0013 195.4762 201.6016 200.7790 209.6572 212.0677 216.6910
#> [49] 214.9681 219.4743 227.0110 235.6320 231.6708 221.9571 221.1423 224.8974
#> [57] 226.8532 229.2295 224.6545 225.5820 222.8428 215.8288 232.1046 229.5814
#> [65] 235.9381 238.6981 245.3815 242.5199 246.9733 246.9832 256.0337 257.4320
#> [73] 263.2717 269.4386 267.8543 271.6152 274.9857 282.5125 288.8002 287.3755
#> [81] 296.9456 295.7621 299.2314 310.4833 311.6350 312.2992 313.8468 323.5312
#> [89] 326.4293 329.7704 327.8913 331.2443 330.7961 336.1438 342.1775 340.5172
#> [97] 349.8119 353.0661 359.7925 360.1808 364.7847 367.1797 369.2604 372.0201
#> [105] 378.9032 378.3931 380.3348 378.8534 378.0331 375.5824 370.9383 362.8547
#> [113] 369.7736 382.4193 386.5488 393.5585 382.9308 390.9163 383.5082 385.2432
#> [121] 397.5069 406.2698 416.8007 417.8449 420.8383 419.4645 429.9997 433.9810
#> [129] 439.0853 437.8677 450.7845 461.3548 456.2693 452.9980 447.8696 471.4062
#> [137] 475.5142 475.0558 477.3729 479.9277 481.6406 489.6361 489.7574 487.5447
#>
#> $series$aligned_sa$new
#> [1] 123.7039 125.1438 125.6189 127.2713 125.9047 125.7578 125.5002 126.2259
#> [9] 128.3286 130.1753 131.3453 130.1246 127.0104 134.1897 133.1920 135.1101
#> [17] 129.6836 138.5674 143.7577 144.5018 149.2902 145.7918 144.1919 154.4849
#> [25] 159.2940 160.7881 166.8819 165.4502 176.4916 164.6686 168.3369 168.7443
#> [33] 174.9166 177.2306 183.4834 183.3867 187.3328 195.5907 185.5886 181.6608
#> [41] 186.9073 200.1135 193.4373 203.6043 199.4804 208.3473 215.3254 215.2287
#> [49] 215.2539 217.9163 226.1554 237.4259 231.9964 221.1382 219.1531 226.8165
#> [57] 225.7667 229.5462 225.5631 224.1952 224.4553 213.6011 232.0176 227.5803
#> [65] 237.1041 237.9385 245.9545 242.4599 245.9748 249.1649 254.7539 255.8904
#> [73] 265.4959 266.6425 265.0050 273.8459 274.4058 281.4297 292.4698 284.9349
#> [81] 295.4729 298.4689 297.8255 311.2879 311.7761 318.7927 314.2138 324.5742
#> [89] 323.5327 331.4021 329.0651 328.6310 335.3923 333.2479 340.1784 344.1716
#> [97] 346.9755 349.3797 363.9722 356.4556 361.4695 372.2019 367.5848 372.9350
#> [105] 380.7826 376.2544 381.4321 379.4460 374.8780 371.7228 369.7398 363.5852
#> [113] 369.2604 383.8987 385.2887 398.4095 381.6716 388.4741 387.8868 382.5857
#> [121] 397.1753 400.9680 416.0246 415.1639 424.6932 417.6227 428.1307 439.4921
#> [129] 438.4003 438.9892 452.4980 459.4851 459.3946 459.2463 442.9714 472.4483
#> [137] 475.5008 473.6975 484.2435 476.9768 481.6046 495.5390 487.9608 490.8070
#>
#> $series$aligned_sa$ok
#> [1] TRUE
#>
#> $series$aligned_sa$reason
#> [1] "ok"
#>
#>
#> $series$aligned_seasonal
#> $series$aligned_seasonal$prev
#> [1] 0.9019909 0.9542175 1.0698054 1.0023238 0.9486743 1.0762915 1.1687498
#> [8] 1.1784737 1.0620085 0.9108650 0.7947027 0.9034620 0.9044253 0.9491504
#> [15] 1.0665026 0.9959364 0.9462061 1.0773716 1.1753610 1.1823424 1.0593844
#> [22] 0.9115370 0.7931061 0.9039952 0.9141398 0.9417802 1.0618930 0.9942760
#> [29] 0.9520064 1.0812071 1.1784129 1.1819479 1.0513155 0.9167609 0.7983953
#> [36] 0.9053339 0.9167921 0.9244155 1.0490375 0.9914602 0.9619895 1.0906339
#> [43] 1.1870718 1.1863075 1.0471047 0.9191085 0.7991875 0.9032417 0.9104194
#> [50] 0.9010165 1.0429827 0.9984889 0.9768772 1.1012838 1.2044124 1.1952541
#> [57] 1.0509099 0.9191185 0.7976929 0.8989483 0.9047060 0.8788380 1.0263130
#> [64] 0.9899166 0.9801523 1.1125437 1.2289230 1.2063680 1.0549012 0.9163132
#> [71] 0.7975557 0.8974620 0.9084206 0.8724821 1.0104347 0.9813505 0.9804222
#> [78] 1.1215922 1.2456033 1.2182118 1.0569141 0.9155538 0.7967154 0.8940588
#> [85] 0.9099794 0.8665834 0.9954911 0.9758214 0.9828361 1.1291156 1.2577076
#> [92] 1.2335300 1.0574570 0.9184159 0.7966728 0.8880924 0.9084879 0.8601435
#> [99] 0.9824858 0.9673156 0.9818267 1.1324750 1.2704668 1.2534584 1.0615283
#> [106] 0.9251866 0.7983848 0.8855795 0.9073863 0.8542446 0.9690255 0.9601876
#> [113] 0.9802351 1.1324730 1.2815049 1.2681131 1.0612632 0.9265177 0.7964929
#> [120] 0.8825474 0.9043100 0.8493213 0.9600489 0.9658416 0.9863019 1.1319021
#> [127] 1.2857469 1.2729665 1.0607040 0.9281347 0.7994991 0.8856520 0.9032140
#> [134] 0.8432997 0.9483270 0.9690174 0.9911469 1.1328470 1.2876816 1.2739133
#> [141] 1.0609690 0.9304721 0.8010242 0.8847667
#>
#> $series$aligned_seasonal$new
#> [1] 0.9053878 0.9429152 1.0615976 1.0032707 0.9610442 1.0734923 1.1792811
#> [8] 1.1725012 1.0597792 0.9141518 0.7918060 0.9068231 0.9054377 0.9389695
#> [15] 1.0592895 0.9985551 0.9638847 1.0752888 1.1825456 1.1764562 1.0583411
#> [22] 0.9122601 0.7906132 0.9062372 0.9102666 0.9329051 1.0556556 0.9954256
#> [29] 0.9745505 1.0809588 1.1821534 1.1792988 1.0519299 0.9140633 0.7957124
#> [36] 0.9051912 0.9128138 0.9202890 1.0463119 0.9902896 0.9790952 1.0893818
#> [43] 1.1890159 1.1885798 1.0477221 0.9167384 0.7987911 0.9013666 0.9105524
#> [50] 0.8994280 1.0384789 0.9945970 0.9870842 1.0988601 1.2046374 1.1992072
#> [57] 1.0497561 0.9192050 0.7980028 0.8965402 0.9088668 0.8801452 1.0232644
#> [64] 0.9873024 0.9869082 1.1095304 1.2278692 1.2084471 1.0529533 0.9190701
#> [71] 0.7968473 0.8949145 0.9115017 0.8738293 1.0095466 0.9803405 0.9839442
#> [78] 1.1192847 1.2445730 1.2178220 1.0559346 0.9180185 0.7957679 0.8930641
#> [85] 0.9109102 0.8689033 0.9984938 0.9743589 0.9828991 1.1285384 1.2550709
#> [92] 1.2323854 1.0584620 0.9182352 0.7966409 0.8890914 0.9078450 0.8615268
#> [99] 0.9881498 0.9663465 0.9821023 1.1337933 1.2650143 1.2522288 1.0609727
#> [106] 0.9222484 0.7996181 0.8855016 0.9069617 0.8554763 0.9756640 0.9604730
#> [113] 0.9830462 1.1331113 1.2743692 1.2675402 1.0585017 0.9241285 0.7992022
#> [120] 0.8808484 0.9064008 0.8529358 0.9658695 0.9637495 0.9889492 1.1302069
#> [127] 1.2799828 1.2719227 1.0561125 0.9271300 0.8000036 0.8814214 0.9077164
#> [134] 0.8513950 0.9556070 0.9658411 0.9926377 1.1294127 1.2844778 1.2705020
#> [141] 1.0548072 0.9303001 0.7992446 0.8801831
#>
#> $series$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 987. 1 0.147 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 987. 1 0.147 log
#>
#> attr(,"class")
#> [1] "seasight_sa_compare"
# }