SARIMA Model Fitting

Usage

ts_sarima(
  x,
  order = NULL,
  seasonal = NULL,
  stepwise = TRUE,
  approximation = TRUE,
  ...
)

Arguments

x: A complete ts_df with no missing values.
order: Optional non-seasonal ARIMA order.
seasonal: Optional seasonal specification.
stepwise, approximation: Passed to forecast::auto.arima().
...: Additional arguments passed to the forecast fitting function.

Value

A list containing tidy model summaries, the raw model, and input.

Examples

ts_sarima(as_ts_df(log(AirPassengers)), stepwise = TRUE, approximation = TRUE)
#> $model_info
#> # A tibble: 1 × 5
#>   log_lik   aic  aicc   bic  sigma2
#>     <dbl> <dbl> <dbl> <dbl>   <dbl>
#> 1    245. -480. -479. -466. 0.00138
#> 
#> $coefficients
#> # A tibble: 4 × 3
#>   term  estimate std_error
#>   <chr>    <dbl>     <dbl>
#> 1 ma1    -0.414     0.0902
#> 2 sar1   -0.137     0.236 
#> 3 sar2   -0.0231    0.156 
#> 4 sma1   -0.457     0.220 
#> 
#> $fitted
#> # A tibble: 144 × 4
#>     time observed fitted residual
#>    <int>    <dbl>  <dbl>    <dbl>
#>  1     1     4.72   4.72 0.00272 
#>  2     2     4.77   4.77 0.00126 
#>  3     3     4.88   4.88 0.000919
#>  4     4     4.86   4.86 0.000665
#>  5     5     4.80   4.80 0.000472
#>  6     6     4.91   4.90 0.000494
#>  7     7     5.00   5.00 0.000508
#>  8     8     5.00   5.00 0.000444
#>  9     9     4.91   4.91 0.000315
#> 10    10     4.78   4.78 0.000156
#> # ℹ 134 more rows
#> 
#> $diagnostics
#> # A tibble: 1 × 5
#>   test        lag statistic p_value conclusion                 
#>   <chr>     <int>     <dbl>   <dbl> <chr>                      
#> 1 Ljung-Box    16      17.3   0.369 No autocorrelation detected
#> 
#> $model
#> Series: x_ts 
#> ARIMA(0,1,1)(2,1,1)[12] 
#> 
#> Coefficients:
#>           ma1     sar1     sar2     sma1
#>       -0.4136  -0.1374  -0.0231  -0.4570
#> s.e.   0.0902   0.2355   0.1562   0.2199
#> 
#> sigma^2 = 0.001385:  log likelihood = 244.97
#> AIC=-479.93   AICc=-479.45   BIC=-465.56
#> 
#> $input
#>     time    value
#> 1      1 4.718499
#> 2      2 4.770685
#> 3      3 4.882802
#> 4      4 4.859812
#> 5      5 4.795791
#> 6      6 4.905275
#> 7      7 4.997212
#> 8      8 4.997212
#> 9      9 4.912655
#> 10    10 4.779123
#> 11    11 4.644391
#> 12    12 4.770685
#> 13    13 4.744932
#> 14    14 4.836282
#> 15    15 4.948760
#> 16    16 4.905275
#> 17    17 4.828314
#> 18    18 5.003946
#> 19    19 5.135798
#> 20    20 5.135798
#> 21    21 5.062595
#> 22    22 4.890349
#> 23    23 4.736198
#> 24    24 4.941642
#> 25    25 4.976734
#> 26    26 5.010635
#> 27    27 5.181784
#> 28    28 5.093750
#> 29    29 5.147494
#> 30    30 5.181784
#> 31    31 5.293305
#> 32    32 5.293305
#> 33    33 5.214936
#> 34    34 5.087596
#> 35    35 4.983607
#> 36    36 5.111988
#> 37    37 5.141664
#> 38    38 5.192957
#> 39    39 5.262690
#> 40    40 5.198497
#> 41    41 5.209486
#> 42    42 5.384495
#> 43    43 5.438079
#> 44    44 5.488938
#> 45    45 5.342334
#> 46    46 5.252273
#> 47    47 5.147494
#> 48    48 5.267858
#> 49    49 5.278115
#> 50    50 5.278115
#> 51    51 5.463832
#> 52    52 5.459586
#> 53    53 5.433722
#> 54    54 5.493061
#> 55    55 5.575949
#> 56    56 5.605802
#> 57    57 5.468060
#> 58    58 5.351858
#> 59    59 5.192957
#> 60    60 5.303305
#> 61    61 5.318120
#> 62    62 5.236442
#> 63    63 5.459586
#> 64    64 5.424950
#> 65    65 5.455321
#> 66    66 5.575949
#> 67    67 5.710427
#> 68    68 5.680173
#> 69    69 5.556828
#> 70    70 5.433722
#> 71    71 5.313206
#> 72    72 5.433722
#> 73    73 5.488938
#> 74    74 5.451038
#> 75    75 5.587249
#> 76    76 5.594711
#> 77    77 5.598422
#> 78    78 5.752573
#> 79    79 5.897154
#> 80    80 5.849325
#> 81    81 5.743003
#> 82    82 5.613128
#> 83    83 5.468060
#> 84    84 5.627621
#> 85    85 5.648974
#> 86    86 5.624018
#> 87    87 5.758902
#> 88    88 5.746203
#> 89    89 5.762051
#> 90    90 5.924256
#> 91    91 6.023448
#> 92    92 6.003887
#> 93    93 5.872118
#> 94    94 5.723585
#> 95    95 5.602119
#> 96    96 5.723585
#> 97    97 5.752573
#> 98    98 5.707110
#> 99    99 5.874931
#> 100  100 5.852202
#> 101  101 5.872118
#> 102  102 6.045005
#> 103  103 6.142037
#> 104  104 6.146329
#> 105  105 6.001415
#> 106  106 5.849325
#> 107  107 5.720312
#> 108  108 5.817111
#> 109  109 5.828946
#> 110  110 5.762051
#> 111  111 5.891644
#> 112  112 5.852202
#> 113  113 5.894403
#> 114  114 6.075346
#> 115  115 6.196444
#> 116  116 6.224558
#> 117  117 6.001415
#> 118  118 5.883322
#> 119  119 5.736572
#> 120  120 5.820083
#> 121  121 5.886104
#> 122  122 5.834811
#> 123  123 6.006353
#> 124  124 5.981414
#> 125  125 6.040255
#> 126  126 6.156979
#> 127  127 6.306275
#> 128  128 6.326149
#> 129  129 6.137727
#> 130  130 6.008813
#> 131  131 5.891644
#> 132  132 6.003887
#> 133  133 6.033086
#> 134  134 5.968708
#> 135  135 6.037871
#> 136  136 6.133398
#> 137  137 6.156979
#> 138  138 6.282267
#> 139  139 6.432940
#> 140  140 6.406880
#> 141  141 6.230481
#> 142  142 6.133398
#> 143  143 5.966147
#> 144  144 6.068426
#>