Function to compute all the multiprocessings or only a given one from a workspace.
By default, the workspace only contains definitions: computation is needed to recalculate and access the adjusted model
(with get_model).
Examples
# \donttest{
spec_x13 <- x13_spec(spec = "RSA5c", easter.enabled = FALSE)
sa_x13 <- x13(ipi_c_eu[, "FR"], spec = spec_x13)
wk <- new_workspace()
mp <- new_multiprocessing(wk, "sap1")
add_sa_item(wk, "sap1", sa_x13, "X13")
sa_item1 <- get_object(mp, 1)
get_model(sa_item1, wk) # Returns NULL
#> Warning: The result of the object is NULL: have you computed the workspace after importing it?
#> See ?compute for more information.
#> NULL
compute(wk)
get_model(sa_item1, wk) # Returns the SA model sa_x13
#> RegARIMA
#> y = regression model + arima (2, 1, 1, 0, 1, 1)
#> Log-transformation: no
#> Coefficients:
#> Estimate Std. Error
#> Phi(1) 0.02043 0.107
#> Phi(2) 0.11093 0.077
#> Theta(1) -0.58663 0.098
#> BTheta(1) -0.69921 0.041
#>
#> Estimate Std. Error
#> Monday 0.6788 0.244
#> Tuesday 0.9500 0.245
#> Wednesday 1.0053 0.246
#> Thursday -0.0656 0.245
#> Friday 1.0304 0.245
#> Saturday -1.5721 0.245
#> Leap year 2.1513 0.753
#> TC (4-2020) -36.1532 2.193
#> AO (3-2020) -20.2213 2.255
#> AO (5-2011) 13.2210 1.970
#> LS (11-2008) -12.7459 1.663
#>
#>
#> Residual standard error: 2.294 on 343 degrees of freedom
#> Log likelihood = -811.8, aic = 1656 aicc = 1657, bic(corrected for length) = 1.907
#>
#>
#>
#> Decomposition
#> Monitoring and Quality Assessment Statistics:
#> M stats
#> M(1) 0.127
#> M(2) 0.076
#> M(3) 1.139
#> M(4) 0.080
#> M(5) 1.072
#> M(6) 0.030
#> M(7) 0.084
#> M(8) 0.244
#> M(9) 0.063
#> M(10) 0.254
#> M(11) 0.239
#> Q 0.319
#> Q-M2 0.349
#>
#> Final filters:
#> Seasonal filter: 3x5
#> Trend filter: 13 terms Henderson moving average
#>
#>
#> Final
#> Last observed values
#> y sa t s i
#> Jan 2020 101.0 102.92412 102.8147 -1.9241199 0.1094395
#> Feb 2020 100.1 103.50547 102.8976 -3.4054722 0.6078825
#> Mar 2020 91.8 83.00218 103.1831 8.7978221 -20.1809275
#> Apr 2020 66.7 65.83747 103.6570 0.8625259 -37.8194996
#> May 2020 73.7 78.73548 104.1282 -5.0354792 -25.3926864
#> Jun 2020 98.2 87.26580 104.5188 10.9342001 -17.2529628
#> Jul 2020 97.4 92.53528 104.7033 4.8647224 -12.1679796
#> Aug 2020 71.7 97.64618 104.5690 -25.9461813 -6.9227978
#> Sep 2020 104.7 97.34380 104.1405 7.3562012 -6.7967236
#> Oct 2020 106.7 98.78451 103.5511 7.9154948 -4.7665946
#> Nov 2020 101.6 100.50933 103.0298 1.0906693 -2.5204281
#> Dec 2020 96.6 99.74645 102.7263 -3.1464483 -2.9798265
#>
#> Forecasts:
#> y_f sa_f t_f s_f i_f
#> Jan 2021 94.82712 101.3420 102.6158 -6.5148928 -1.27380795
#> Feb 2021 98.00197 101.8372 102.5826 -3.8352640 -0.74540432
#> Mar 2021 113.62540 101.9088 102.5235 11.7165747 -0.61463800
#> Apr 2021 103.28588 102.3855 102.3987 0.9004327 -0.01326784
#> May 2021 96.23640 101.5938 102.2898 -5.3574428 -0.69592946
#> Jun 2021 113.04727 101.6902 102.2365 11.3570720 -0.54632230
#> Jul 2021 104.27161 101.8281 102.2910 2.4435091 -0.46291753
#> Aug 2021 79.29357 102.6462 102.4831 -23.3526340 0.16311885
#> Sep 2021 109.23197 102.7863 102.7326 6.4456639 0.05371199
#> Oct 2021 108.79200 102.9386 102.9781 5.8533505 -0.03947210
#> Nov 2021 106.90616 102.9549 103.2017 3.9512094 -0.24675320
#> Dec 2021 100.22813 103.5456 103.3775 -3.3174206 0.16802658
#>
#>
#> Diagnostics
#> Relative contribution of the components to the stationary
#> portion of the variance in the original series,
#> after the removal of the long term trend
#> Trend computed by Hodrick-Prescott filter (cycle length = 8.0 years)
#> Component
#> Cycle 2.238
#> Seasonal 59.768
#> Irregular 1.203
#> TD & Hol. 2.436
#> Others 34.383
#> Total 100.028
#>
#> Combined test in the entire series
#> Non parametric tests for stable seasonality
#> P.value
#> Kruskall-Wallis test 0.000
#> Test for the presence of seasonality assuming stability 0.000
#> Evolutive seasonality test 0.059
#>
#> Identifiable seasonality present
#>
#> Residual seasonality tests
#> P.value
#> qs test on sa 1.000
#> qs test on i 0.985
#> f-test on sa (seasonal dummies) 0.916
#> f-test on i (seasonal dummies) 0.812
#> Residual seasonality (entire series) 0.902
#> Residual seasonality (last 3 years) 0.966
#> f-test on sa (td) 0.983
#> f-test on i (td) 0.998
#>
#>
#> Additional output variables
# }