This vignette explains briefly how to use the function adam() and the related auto.adam() in smooth package. It does not aim at covering all aspects of the function, but focuses on the main ones.

ADAM is Augmented Dynamic Adaptive Model. It is a model that underlies ETS, ARIMA and regression, connecting them in a unified framework. The underlying model for ADAM is a Single Source of Error state space model, which is explained in detail separately in an online textbook.

The main philosophy of adam() function is to be agnostic of the provided data. This means that it will work with ts, msts, zoo, xts, data.frame, numeric and other classes of data. The specification of seasonality in the model is done using a separate parameter lags, so you are not obliged to transform the existing data to something specific, and can use it as is. If you provide a matrix, or a data.frame, or a data.table, or any other multivariate structure, then the function will use the first column for the response variable and the others for the explanatory ones. One thing that is currently assumed in the function is that the data is measured at a regular frequency. If this is not the case, you will need to introduce missing values manually.

In order to run the experiments in this vignette, we need to load the following packages:

ADAM ETS

First and foremost, ADAM implements ETS model, although in a more flexible way than (Hyndman et al. 2008): it supports different distributions for the error term, which are regulated via distribution parameter. By default, the additive error model relies on Normal distribution, while the multiplicative error one assumes Inverse Gaussian. If you want to reproduce the classical ETS, you would need to specify distribution="dnorm". Here is an example of ADAM ETS(MMM) with Normal distribution on AirPassengers data:

testModel <- adam(AirPassengers, "MMM", lags=c(1,12), distribution="dnorm",
                  h=12, holdout=TRUE)
summary(testModel)
#> 
#> Model estimated using adam() function: ETS(MMM)
#> Response variable: AirPassengers
#> Distribution used in the estimation: Normal
#> Loss function type: likelihood; Loss function value: 468.9192
#> Coefficients:
#>             Estimate Std. Error Lower 2.5% Upper 97.5%  
#> alpha         0.8451     0.0844     0.6780      1.0000 *
#> beta          0.0205     0.0265     0.0000      0.0727  
#> gamma         0.0000     0.0373     0.0000      0.0736  
#> level       120.2667    14.4276    91.6885    148.7758 *
#> trend         1.0017     0.0100     0.9819      1.0216 *
#> seasonal_1    0.9132     0.0078     0.8987      0.9365 *
#> seasonal_2    0.8996     0.0082     0.8851      0.9229 *
#> seasonal_3    1.0301     0.0096     1.0156      1.0533 *
#> seasonal_4    0.9866     0.0084     0.9721      1.0098 *
#> seasonal_5    0.9852     0.0073     0.9707      1.0085 *
#> seasonal_6    1.1164     0.0095     1.1019      1.1397 *
#> seasonal_7    1.2328     0.0118     1.2183      1.2561 *
#> seasonal_8    1.2262     0.0109     1.2117      1.2495 *
#> seasonal_9    1.0671     0.0093     1.0527      1.0904 *
#> seasonal_10   0.9279     0.0090     0.9134      0.9512 *
#> seasonal_11   0.8058     0.0078     0.7914      0.8291 *
#> 
#> Error standard deviation: 0.0376
#> Sample size: 132
#> Number of estimated parameters: 17
#> Number of degrees of freedom: 115
#> Information criteria:
#>       AIC      AICc       BIC      BICc 
#>  971.8385  977.2069 1020.8461 1033.9526
plot(forecast(testModel,h=12,interval="prediction"))

You might notice that the summary contains more than what is reported by other smooth functions. This one also produces standard errors for the estimated parameters based on Fisher Information calculation. Note that this is computationally expensive, so if you have a model with more than 30 variables, the calculation of standard errors might take plenty of time. As for the default print() method, it will produce a shorter summary from the model, without the standard errors (similar to what es() does):

testModel
#> Time elapsed: 0.12 seconds
#> Model estimated using adam() function: ETS(MMM)
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 468.9192
#> Persistence vector g:
#>  alpha   beta  gamma 
#> 0.8451 0.0205 0.0000 
#> 
#> Sample size: 132
#> Number of estimated parameters: 17
#> Number of degrees of freedom: 115
#> Information criteria:
#>       AIC      AICc       BIC      BICc 
#>  971.8385  977.2069 1020.8461 1033.9526 
#> 
#> Forecast errors:
#> ME: -4.532; MAE: 15.668; RMSE: 21.652
#> sCE: -20.72%; Asymmetry: -12.1%; sMAE: 5.969%; sMSE: 0.68%
#> MASE: 0.651; RMSSE: 0.691; rMAE: 0.206; rRMSE: 0.21

Also, note that the prediction interval in case of multiplicative error models are approximate. It is advisable to use simulations instead (which is slower, but more accurate):

plot(forecast(testModel,h=18,interval="simulated"))

If you want to do the residuals diagnostics, then it is recommended to use plot function, something like this (you can select, which of the plots to produce):

par(mfcol=c(3,4))
plot(testModel,which=c(1:11))
par(mfcol=c(1,1))
plot(testModel,which=12)

By default ADAM will estimate models via maximising likelihood function. But there is also a parameter loss, which allows selecting from a list of already implemented loss functions (again, see documentation for adam() for the full list) or using a function written by a user. Here is how to do the latter on the example of BJsales:

lossFunction <- function(actual, fitted, B){
  return(sum(abs(actual-fitted)^3))
}
testModel <- adam(BJsales, "AAN", silent=FALSE, loss=lossFunction,
                  h=12, holdout=TRUE)
testModel
#> Time elapsed: 0.02 seconds
#> Model estimated using adam() function: ETS(AAN)
#> Distribution assumed in the model: Normal
#> Loss function type: custom; Loss function value: 599.2829
#> Persistence vector g:
#>  alpha   beta 
#> 1.0000 0.2253 
#> 
#> Sample size: 138
#> Number of estimated parameters: 4
#> Number of degrees of freedom: 134
#> Information criteria are unavailable for the chosen loss & distribution.
#> 
#> Forecast errors:
#> ME: 3.02; MAE: 3.133; RMSE: 3.871
#> sCE: 15.942%; Asymmetry: 91.8%; sMAE: 1.378%; sMSE: 0.029%
#> MASE: 2.63; RMSSE: 2.523; rMAE: 1.011; rRMSE: 1.01

Note that you need to have parameters actual, fitted and B in the function, which correspond to the vector of actual values, vector of fitted values on each iteration and a vector of the optimised parameters.

loss and distribution parameters are independent, so in the example above, we have assumed that the error term follows Normal distribution, but we have estimated its parameters using a non-conventional loss because we can. Some of distributions assume that there is an additional parameter, which can either be estimated or provided by user. These include Asymmetric Laplace (distribution="dalaplace") with alpha, Generalised Normal and Log-Generalised normal (distribution=c("gnorm","dlgnorm")) with shape and Student’s T (distribution="dt") with nu:

testModel <- adam(BJsales, "MMN", silent=FALSE, distribution="dgnorm", shape=3,
                  h=12, holdout=TRUE)

The model selection in ADAM ETS relies on information criteria and works correctly only for the loss="likelihood". There are several options, how to select the model, see them in the description of the function: ?adam(). The default one uses branch-and-bound algorithm, similar to the one used in es(), but only considers additive trend models (the multiplicative trend ones are less stable and need more attention from a forecaster):

testModel <- adam(AirPassengers, "ZXZ", lags=c(1,12), silent=FALSE,
                  h=12, holdout=TRUE)
#> Forming the pool of models based on... ANN , ANA , MNM , MAM , Estimation progress:    71 %86 %100 %... Done!
testModel
#> Time elapsed: 0.54 seconds
#> Model estimated using adam() function: ETS(MAM)
#> Distribution assumed in the model: Gamma
#> Loss function type: likelihood; Loss function value: 467.2981
#> Persistence vector g:
#>  alpha   beta  gamma 
#> 0.7691 0.0053 0.0000 
#> 
#> Sample size: 132
#> Number of estimated parameters: 17
#> Number of degrees of freedom: 115
#> Information criteria:
#>       AIC      AICc       BIC      BICc 
#>  968.5961  973.9646 1017.6038 1030.7102 
#> 
#> Forecast errors:
#> ME: 9.537; MAE: 20.784; RMSE: 26.106
#> sCE: 43.598%; Asymmetry: 64.8%; sMAE: 7.918%; sMSE: 0.989%
#> MASE: 0.863; RMSSE: 0.833; rMAE: 0.273; rRMSE: 0.254

Note that the function produces point forecasts if h>0, but it won’t generate prediction interval. This is why you need to use forecast() method (as shown in the first example in this vignette).

Similarly to es(), function supports combination of models, but it saves all the tested models in the output for a potential reuse. Here how it works:

testModel <- adam(AirPassengers, "CXC", lags=c(1,12),
                  h=12, holdout=TRUE)
testForecast <- forecast(testModel,h=18,interval="semiparametric", level=c(0.9,0.95))
testForecast
#>          Point forecast Lower bound (5%) Lower bound (2.5%) Upper bound (95%)
#> Jan 1960       412.6034         389.5437           385.2566          436.2179
#> Feb 1960       407.6802         378.6486           373.2973          437.6078
#> Mar 1960       469.9434         429.7409           422.3908          511.6467
#> Apr 1960       453.0199         410.9431           403.2828          496.8081
#> May 1960       453.8249         410.5663           402.7021          498.8921
#> Jun 1960       515.8123         464.1523           454.7877          569.7485
#> Jul 1960       572.4694         512.5017           501.6610          635.2091
#> Aug 1960       571.1605         509.5480           498.4311          635.7132
#> Sep 1960       498.8989         444.3880           434.5612          556.0492
#> Oct 1960       436.6050         387.8756           379.1038          487.7499
#> Nov 1960       380.8949         337.5949           329.8107          426.3861
#> Dec 1960       429.4566         378.8850           369.8167          482.6886
#> Jan 1961       436.9883         382.4494           372.7118          494.5810
#> Feb 1961       431.6564         374.1751           363.9648          492.5871
#> Mar 1961       497.4465         426.2819           413.7186          573.2226
#> Apr 1961       479.4040         407.0246           394.3104          556.7548
#> May 1961       480.1289         406.0746           393.0946          559.3951
#> Jun 1961       545.5651         459.4616           444.4065          637.8924
#>          Upper bound (97.5%)
#> Jan 1960            440.8744
#> Feb 1960            443.5558
#> Mar 1960            519.9962
#> Apr 1960            505.6080
#> May 1960            507.9605
#> Jun 1960            580.6289
#> Jul 1960            647.8957
#> Aug 1960            648.7880
#> Sep 1960            567.6336
#> Oct 1960            498.1301
#> Nov 1960            435.6293
#> Dec 1960            493.5283
#> Jan 1961            506.3517
#> Feb 1961            505.0939
#> Mar 1961            588.8560
#> Apr 1961            572.7783
#> May 1961            575.8447
#> Jun 1961            657.0907
plot(testForecast)

Yes, now we support vectors for the levels in case you want to produce several. In fact, we also support side for prediction interval, so you can extract specific quantiles without a hustle:

forecast(testModel,h=18,interval="semiparametric", level=c(0.9,0.95,0.99), side="upper")
#>          Point forecast Upper bound (90%) Upper bound (95%) Upper bound (99%)
#> Jan 1960       412.6034          430.8898          436.2179          446.3299
#> Feb 1960       407.6802          430.8160          437.6078          450.5387
#> Mar 1960       469.9434          502.1309          511.6467          529.8170
#> Apr 1960       453.0199          486.7889          496.8081          515.9685
#> May 1960       453.8249          488.5705          498.8921          518.6407
#> Jun 1960       515.8123          557.3728          569.7485          593.4512
#> Jul 1960       572.4694          620.7878          635.2091          662.8558
#> Aug 1960       571.1605          620.8570          635.7132          664.2124
#> Sep 1960       498.8989          542.8891          556.0492          581.3025
#> Oct 1960       436.6050          475.9617          487.7499          510.3821
#> Nov 1960       380.8949          415.8921          426.3861          446.5425
#> Dec 1960       429.4566          470.3891          482.6886          506.3336
#> Jan 1961       436.9883          481.2378          494.5810          520.2698
#> Feb 1961       431.6564          478.4253          492.5871          519.8986
#> Mar 1961       497.4465          555.5437          573.2226          607.3855
#> Apr 1961       479.4040          538.6537          556.7548          591.7900
#> May 1961       480.1289          540.8213          559.3951          595.3706
#> Jun 1961       545.5651          616.2262          637.8924          679.8907

A brand new thing in the function is the possibility to use several frequencies (double / triple / quadruple / … seasonal models). In order to show how it works, we will generate an artificial time series, inspired by half-hourly electricity demand using sim.gum() function:

ordersGUM <- c(1,1,1)
lagsGUM <- c(1,48,336)
initialGUM1 <- -25381.7
initialGUM2 <- c(23955.09, 24248.75, 24848.54, 25012.63, 24634.14, 24548.22, 24544.63, 24572.77,
                 24498.33, 24250.94, 24545.44, 25005.92, 26164.65, 27038.55, 28262.16, 28619.83,
                 28892.19, 28575.07, 28837.87, 28695.12, 28623.02, 28679.42, 28682.16, 28683.40,
                 28647.97, 28374.42, 28261.56, 28199.69, 28341.69, 28314.12, 28252.46, 28491.20,
                 28647.98, 28761.28, 28560.11, 28059.95, 27719.22, 27530.23, 27315.47, 27028.83,
                 26933.75, 26961.91, 27372.44, 27362.18, 27271.31, 26365.97, 25570.88, 25058.01)
initialGUM3 <- c(23920.16, 23026.43, 22812.23, 23169.52, 23332.56, 23129.27, 22941.20, 22692.40,
                 22607.53, 22427.79, 22227.64, 22580.72, 23871.99, 25758.34, 28092.21, 30220.46,
                 31786.51, 32699.80, 33225.72, 33788.82, 33892.25, 34112.97, 34231.06, 34449.53,
                 34423.61, 34333.93, 34085.28, 33948.46, 33791.81, 33736.17, 33536.61, 33633.48,
                 33798.09, 33918.13, 33871.41, 33403.75, 32706.46, 31929.96, 31400.48, 30798.24,
                 29958.04, 30020.36, 29822.62, 30414.88, 30100.74, 29833.49, 28302.29, 26906.72,
                 26378.64, 25382.11, 25108.30, 25407.07, 25469.06, 25291.89, 25054.11, 24802.21,
                 24681.89, 24366.97, 24134.74, 24304.08, 25253.99, 26950.23, 29080.48, 31076.33,
                 32453.20, 33232.81, 33661.61, 33991.21, 34017.02, 34164.47, 34398.01, 34655.21,
                 34746.83, 34596.60, 34396.54, 34236.31, 34153.32, 34102.62, 33970.92, 34016.13,
                 34237.27, 34430.08, 34379.39, 33944.06, 33154.67, 32418.62, 31781.90, 31208.69,
                 30662.59, 30230.67, 30062.80, 30421.11, 30710.54, 30239.27, 28949.56, 27506.96,
                 26891.75, 25946.24, 25599.88, 25921.47, 26023.51, 25826.29, 25548.72, 25405.78,
                 25210.45, 25046.38, 24759.76, 24957.54, 25815.10, 27568.98, 29765.24, 31728.25,
                 32987.51, 33633.74, 34021.09, 34407.19, 34464.65, 34540.67, 34644.56, 34756.59,
                 34743.81, 34630.05, 34506.39, 34319.61, 34110.96, 33961.19, 33876.04, 33969.95,
                 34220.96, 34444.66, 34474.57, 34018.83, 33307.40, 32718.90, 32115.27, 31663.53,
                 30903.82, 31013.83, 31025.04, 31106.81, 30681.74, 30245.70, 29055.49, 27582.68,
                 26974.67, 25993.83, 25701.93, 25940.87, 26098.63, 25771.85, 25468.41, 25315.74,
                 25131.87, 24913.15, 24641.53, 24807.15, 25760.85, 27386.39, 29570.03, 31634.00,
                 32911.26, 33603.94, 34020.90, 34297.65, 34308.37, 34504.71, 34586.78, 34725.81,
                 34765.47, 34619.92, 34478.54, 34285.00, 34071.90, 33986.48, 33756.85, 33799.37,
                 33987.95, 34047.32, 33924.48, 33580.82, 32905.87, 32293.86, 31670.02, 31092.57,
                 30639.73, 30245.42, 30281.61, 30484.33, 30349.51, 29889.23, 28570.31, 27185.55,
                 26521.85, 25543.84, 25187.82, 25371.59, 25410.07, 25077.67, 24741.93, 24554.62,
                 24427.19, 24127.21, 23887.55, 24028.40, 24981.34, 26652.32, 28808.00, 30847.09,
                 32304.13, 33059.02, 33562.51, 33878.96, 33976.68, 34172.61, 34274.50, 34328.71,
                 34370.12, 34095.69, 33797.46, 33522.96, 33169.94, 32883.32, 32586.24, 32380.84,
                 32425.30, 32532.69, 32444.24, 32132.49, 31582.39, 30926.58, 30347.73, 29518.04,
                 29070.95, 28586.20, 28416.94, 28598.76, 28529.75, 28424.68, 27588.76, 26604.13,
                 26101.63, 25003.82, 24576.66, 24634.66, 24586.21, 24224.92, 23858.42, 23577.32,
                 23272.28, 22772.00, 22215.13, 21987.29, 21948.95, 22310.79, 22853.79, 24226.06,
                 25772.55, 27266.27, 28045.65, 28606.14, 28793.51, 28755.83, 28613.74, 28376.47,
                 27900.76, 27682.75, 27089.10, 26481.80, 26062.94, 25717.46, 25500.27, 25171.05,
                 25223.12, 25634.63, 26306.31, 26822.46, 26787.57, 26571.18, 26405.21, 26148.41,
                 25704.47, 25473.10, 25265.97, 26006.94, 26408.68, 26592.04, 26224.64, 25407.27,
                 25090.35, 23930.21, 23534.13, 23585.75, 23556.93, 23230.25, 22880.24, 22525.52,
                 22236.71, 21715.08, 21051.17, 20689.40, 20099.18, 19939.71, 19722.69, 20421.58,
                 21542.03, 22962.69, 23848.69, 24958.84, 25938.72, 26316.56, 26742.61, 26990.79,
                 27116.94, 27168.78, 26464.41, 25703.23, 25103.56, 24891.27, 24715.27, 24436.51,
                 24327.31, 24473.02, 24893.89, 25304.13, 25591.77, 25653.00, 25897.55, 25859.32,
                 25918.32, 25984.63, 26232.01, 26810.86, 27209.70, 26863.50, 25734.54, 24456.96)
y <- sim.gum(orders=ordersGUM, lags=lagsGUM, nsim=1, frequency=336, obs=3360,
             measurement=rep(1,3), transition=diag(3), persistence=c(0.045,0.162,0.375),
             initial=cbind(initialGUM1,initialGUM2,initialGUM3))$data

We can then apply ADAM to this data:

testModel <- adam(y, "MMdM", lags=c(1,48,336), initial="backcasting",
                  silent=FALSE, h=336, holdout=TRUE)
testModel
#> Time elapsed: 0.71 seconds
#> Model estimated using adam() function: ETS(MMdM)[48, 336]
#> Distribution assumed in the model: Gamma
#> Loss function type: likelihood; Loss function value: 22062.57
#> Persistence vector g:
#>  alpha   beta gamma1 gamma2 
#> 0.9771 0.0000 0.0229 0.0228 
#> Damping parameter: 0.9867
#> Sample size: 3024
#> Number of estimated parameters: 6
#> Number of degrees of freedom: 3018
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 44137.14 44137.17 44173.23 44173.34 
#> 
#> Forecast errors:
#> ME: -338.528; MAE: 767.123; RMSE: 1002.159
#> sCE: -373.935%; Asymmetry: -46.2%; sMAE: 2.522%; sMSE: 0.109%
#> MASE: 1.015; RMSSE: 0.966; rMAE: 0.122; rRMSE: 0.129

Note that the more lags you have, the more initial seasonal components the function will need to estimate, which is a difficult task. This is why we used initial="backcasting" in the example above - this speeds up the estimation by reducing the number of parameters to estimate. Still, the optimiser might not get close to the optimal value, so we can help it. First, we can give more time for the calculation, increasing the number of iterations via maxeval (the default value is 40 iterations for each estimated parameter, e.g. $40 \times 5 = 200$ in our case):

testModel <- adam(y, "MMdM", lags=c(1,48,336), initial="backcasting",
                  silent=FALSE, h=336, holdout=TRUE, maxeval=10000)
testModel
#> Time elapsed: 1.96 seconds
#> Model estimated using adam() function: ETS(MMdM)[48, 336]
#> Distribution assumed in the model: Gamma
#> Loss function type: likelihood; Loss function value: 22062.54
#> Persistence vector g:
#>  alpha   beta gamma1 gamma2 
#> 0.9771 0.0000 0.0229 0.0229 
#> Damping parameter: 0.9882
#> Sample size: 3024
#> Number of estimated parameters: 6
#> Number of degrees of freedom: 3018
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 44137.08 44137.11 44173.17 44173.28 
#> 
#> Forecast errors:
#> ME: -338.524; MAE: 767.12; RMSE: 1002.156
#> sCE: -373.931%; Asymmetry: -46.2%; sMAE: 2.522%; sMSE: 0.109%
#> MASE: 1.015; RMSSE: 0.966; rMAE: 0.122; rRMSE: 0.129

This will take more time, but will typically lead to more refined parameters. You can control other parameters of the optimiser as well, such as algorithm, xtol_rel, print_level and others, which are explained in the documentation for nloptr function from nloptr package (run nloptr.print.options() for details). Second, we can give a different set of initial parameters for the optimiser, have a look at what the function saves:

testModel$B

and use this as a starting point for the reestimation (e.g. with a different algorithm):

testModel <- adam(y, "MMdM", lags=c(1,48,336), initial="backcasting",
                  silent=FALSE, h=336, holdout=TRUE, B=testModel$B)
testModel
#> Time elapsed: 0.58 seconds
#> Model estimated using adam() function: ETS(MMdM)[48, 336]
#> Distribution assumed in the model: Gamma
#> Loss function type: likelihood; Loss function value: 22062.54
#> Persistence vector g:
#>  alpha   beta gamma1 gamma2 
#> 0.9771 0.0000 0.0229 0.0229 
#> Damping parameter: 0.9903
#> Sample size: 3024
#> Number of estimated parameters: 6
#> Number of degrees of freedom: 3018
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 44137.08 44137.11 44173.17 44173.28 
#> 
#> Forecast errors:
#> ME: -338.524; MAE: 767.12; RMSE: 1002.156
#> sCE: -373.931%; Asymmetry: -46.2%; sMAE: 2.522%; sMSE: 0.109%
#> MASE: 1.015; RMSSE: 0.966; rMAE: 0.122; rRMSE: 0.129

If you are ready to wait, you can change the initialisation to the initial="optimal", which in our case will take much more time because of the number of estimated parameters - 389 for the chosen model. The estimation process in this case might take 20 - 30 times more than in the example above.

In addition, you can specify some parts of the initial state vector or some parts of the persistence vector, here is an example:

testModel <- adam(y, "MMdM", lags=c(1,48,336), initial="backcasting",
                  silent=TRUE, h=336, holdout=TRUE, persistence=list(beta=0.1))
testModel
#> Time elapsed: 0.64 seconds
#> Model estimated using adam() function: ETS(MMdM)[48, 336]
#> Distribution assumed in the model: Gamma
#> Loss function type: likelihood; Loss function value: 21838.73
#> Persistence vector g:
#>  alpha   beta gamma1 gamma2 
#> 0.8941 0.1000 0.1050 0.0329 
#> Damping parameter: 0.6402
#> Sample size: 3024
#> Number of estimated parameters: 5
#> Number of degrees of freedom: 3019
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 43687.46 43687.48 43717.53 43717.61 
#> 
#> Forecast errors:
#> ME: 21.642; MAE: 727.955; RMSE: 957.594
#> sCE: 23.906%; Asymmetry: 3.9%; sMAE: 2.393%; sMSE: 0.099%
#> MASE: 0.964; RMSSE: 0.923; rMAE: 0.116; rRMSE: 0.123

The function also handles intermittent data (the data with zeroes) and the data with missing values. This is partially covered in the vignette on the oes() function. Here is a simple example:

testModel <- adam(rpois(120,0.5), "MNN", silent=FALSE, h=12, holdout=TRUE,
                  occurrence="odds-ratio")
testModel
#> Time elapsed: 0.04 seconds
#> Model estimated using adam() function: iETS(MNN)[O]
#> Occurrence model type: Odds ratio
#> Distribution assumed in the model: Mixture of Bernoulli and Gamma
#> Loss function type: likelihood; Loss function value: 45.8565
#> Persistence vector g:
#> alpha 
#>     0 
#> 
#> Sample size: 108
#> Number of estimated parameters: 5
#> Number of degrees of freedom: 103
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 241.8075 242.0382 255.2181 246.3941 
#> 
#> Forecast errors:
#> Asymmetry: -54.017%; sMSE: 12.868%; rRMSE: 0.944; sPIS: 1455.386%; sCE: -171.29%

Finally, adam() is faster than es() function, because its code is more efficient and it uses a different optimisation algorithm with more finely tuned parameters by default. Let’s compare:

adamModel <- adam(AirPassengers, "CCC",
                  h=12, holdout=TRUE)
esModel <- es(AirPassengers, "CCC",
              h=12, holdout=TRUE)
"adam:"
#> [1] "adam:"
adamModel
#> Time elapsed: 2.19 seconds
#> Model estimated: ETS(CCC)
#> Loss function type: likelihood
#> 
#> Number of models combined: 30
#> Sample size: 132
#> Average number of estimated parameters: 21.8083
#> Average number of degrees of freedom: 110.1917
#> 
#> Forecast errors:
#> ME: -1.759; MAE: 14.402; RMSE: 20.569
#> sCE: -8.04%; sMAE: 5.487%; sMSE: 0.614%
#> MASE: 0.598; RMSSE: 0.656; rMAE: 0.19; rRMSE: 0.2
"es():"
#> [1] "es():"
esModel
#> Time elapsed: 2.1 seconds
#> Model estimated: ETS(CCC)
#> Loss function type: likelihood
#> 
#> Number of models combined: 30
#> Sample size: 132
#> Average number of estimated parameters: 21.3232
#> Average number of degrees of freedom: 110.6768
#> 
#> Forecast errors:
#> ME: 2.906; MAE: 15.858; RMSE: 22.111
#> sCE: 13.287%; sMAE: 6.041%; sMSE: 0.71%
#> MASE: 0.658; RMSSE: 0.706; rMAE: 0.209; rRMSE: 0.215

ADAM ARIMA

As mentioned above, ADAM does not only contain ETS, it also contains ARIMA model, which is regulated via orders parameter. If you want to have a pure ARIMA, you need to switch off ETS, which is done via model="NNN":

testModel <- adam(BJsales, "NNN", silent=FALSE, orders=c(0,2,2),
                  h=12, holdout=TRUE)
testModel
#> Time elapsed: 0.04 seconds
#> Model estimated using adam() function: ARIMA(0,2,2)
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 240.5944
#> ARMA parameters of the model:
#> MA:
#> theta1[1] theta2[1] 
#>   -0.7484   -0.0165 
#> 
#> Sample size: 138
#> Number of estimated parameters: 5
#> Number of degrees of freedom: 133
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 491.1888 491.6434 505.8251 506.9449 
#> 
#> Forecast errors:
#> ME: 2.961; MAE: 3.087; RMSE: 3.812
#> sCE: 15.63%; Asymmetry: 90.2%; sMAE: 1.358%; sMSE: 0.028%
#> MASE: 2.591; RMSSE: 2.485; rMAE: 0.996; rRMSE: 0.995

Given that both models are implemented in the same framework, they can be compared using information criteria.

The functionality of ADAM ARIMA is similar to the one of msarima function in smooth package, although there are several differences.

First, changing the distribution parameter will allow switching between additive / multiplicative models. For example, distribution="dlnorm" will create an ARIMA, equivalent to the one on logarithms of the data:

testModel <- adam(AirPassengers, "NNN", silent=FALSE, lags=c(1,12),
                  orders=list(ar=c(1,1),i=c(1,1),ma=c(2,2)), distribution="dlnorm",
                  h=12, holdout=TRUE)
testModel
#> Time elapsed: 0.62 seconds
#> Model estimated using adam() function: SARIMA(1,1,2)[1](1,1,2)[12]
#> Distribution assumed in the model: Log-Normal
#> Loss function type: likelihood; Loss function value: 497.8476
#> ARMA parameters of the model:
#> AR:
#>  phi1[1] phi1[12] 
#>  -0.2329  -0.0467 
#> MA:
#>  theta1[1]  theta2[1] theta1[12] theta2[12] 
#>     0.2091     0.1830    -0.3077    -0.0148 
#> 
#> Sample size: 132
#> Number of estimated parameters: 33
#> Number of degrees of freedom: 99
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 1061.695 1084.593 1156.828 1212.731 
#> 
#> Forecast errors:
#> ME: -24.727; MAE: 24.727; RMSE: 28.993
#> sCE: -113.041%; Asymmetry: -100%; sMAE: 9.42%; sMSE: 1.22%
#> MASE: 1.027; RMSSE: 0.925; rMAE: 0.325; rRMSE: 0.282

Second, if you want the model with intercept / drift, you can do it using constant parameter:

testModel <- adam(AirPassengers, "NNN", silent=FALSE, lags=c(1,12), constant=TRUE,
                  orders=list(ar=c(1,1),i=c(1,1),ma=c(2,2)), distribution="dnorm",
                  h=12, holdout=TRUE)
testModel
#> Time elapsed: 0.43 seconds
#> Model estimated using adam() function: SARIMA(1,1,2)[1](1,1,2)[12] with drift
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 501.6243
#> ARMA parameters of the model:
#> AR:
#>  phi1[1] phi1[12] 
#>   0.6168  -0.0477 
#> MA:
#>  theta1[1]  theta2[1] theta1[12] theta2[12] 
#>    -0.6855     0.2281    -0.0213     0.1886 
#> 
#> Sample size: 132
#> Number of estimated parameters: 34
#> Number of degrees of freedom: 98
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 1071.249 1095.785 1169.264 1229.166 
#> 
#> Forecast errors:
#> ME: -47.026; MAE: 47.026; RMSE: 53.945
#> sCE: -214.983%; Asymmetry: -100%; sMAE: 17.915%; sMSE: 4.224%
#> MASE: 1.953; RMSSE: 1.722; rMAE: 0.619; rRMSE: 0.524

If the model contains non-zero differences, then the constant acts as a drift. Third, you can specify parameters of ARIMA via the arma parameter in the following manner:

testModel <- adam(AirPassengers, "NNN", silent=FALSE, lags=c(1,12),
                  orders=list(ar=c(1,1),i=c(1,1),ma=c(2,2)), distribution="dnorm",
                  arma=list(ar=c(0.1,0.1), ma=c(-0.96, 0.03, -0.12, 0.03)),
                  h=12, holdout=TRUE)
testModel
#> Time elapsed: 0.26 seconds
#> Model estimated using adam() function: SARIMA(1,1,2)[1](1,1,2)[12]
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 534.9627
#> ARMA parameters of the model:
#> AR:
#>  phi1[1] phi1[12] 
#>      0.1      0.1 
#> MA:
#>  theta1[1]  theta2[1] theta1[12] theta2[12] 
#>      -0.96       0.03      -0.12       0.03 
#> 
#> Sample size: 132
#> Number of estimated parameters: 27
#> Number of degrees of freedom: 105
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 1123.925 1138.464 1201.761 1237.255 
#> 
#> Forecast errors:
#> ME: 9.575; MAE: 17.082; RMSE: 19.148
#> sCE: 43.773%; Asymmetry: 61.9%; sMAE: 6.508%; sMSE: 0.532%
#> MASE: 0.709; RMSSE: 0.611; rMAE: 0.225; rRMSE: 0.186

Finally, the initials for the states can also be provided, although getting the correct ones might be a challenging task (you also need to know how many of them to provide; checking testModel$initial might help):

testModel <- adam(AirPassengers, "NNN", silent=FALSE, lags=c(1,12),
                  orders=list(ar=c(1,1),i=c(1,1),ma=c(2,0)), distribution="dnorm",
                  initial=list(arima=AirPassengers[1:24]),
                  h=12, holdout=TRUE)
testModel
#> Time elapsed: 0.41 seconds
#> Model estimated using adam() function: SARIMA(1,1,2)[1](1,1,0)[12]
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 489.1803
#> ARMA parameters of the model:
#> AR:
#>  phi1[1] phi1[12] 
#>  -0.4281  -0.0469 
#> MA:
#> theta1[1] theta2[1] 
#>    0.2213    0.0414 
#> 
#> Sample size: 132
#> Number of estimated parameters: 31
#> Number of degrees of freedom: 101
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 1040.361 1060.201 1129.727 1178.165 
#> 
#> Forecast errors:
#> ME: -16.429; MAE: 18.213; RMSE: 23.439
#> sCE: -75.107%; Asymmetry: -88.2%; sMAE: 6.938%; sMSE: 0.797%
#> MASE: 0.756; RMSSE: 0.748; rMAE: 0.24; rRMSE: 0.228

If you work with ADAM ARIMA model, then there is no such thing as “usual” bounds for the parameters, so the function will use the bounds="admissible", checking the AR / MA polynomials in order to make sure that the model is stationary and invertible (aka stable).

Similarly to ETS, you can use different distributions and losses for the estimation. Note that the order selection for ARIMA is done in auto.adam() function, not in the adam()! However, if you do orders=list(..., select=TRUE) in adam(), it will call auto.adam() and do the selection.

Finally, ARIMA is typically slower than ETS, mainly because its initial states are more difficult to estimate due to an increased complexity of the model. If you want to speed things up, use initial="backcasting" and reduce the number of iterations via maxeval parameter.

ADAM ETSX / ARIMAX / ETSX+ARIMA

Another important feature of ADAM is introduction of explanatory variables. Unlike in es(), adam() expects a matrix for data and can work with a formula. If the latter is not provided, then it will use all explanatory variables. Here is a brief example:

BJData <- cbind(BJsales,BJsales.lead)
testModel <- adam(BJData, "AAN", h=18, silent=FALSE)

If you work with data.frame or similar structures, then you can use them directly, ADAM will extract the response variable either assuming that it is in the first column or from the provided formula (if you specify one via formula parameter). Here is an example, where we create a matrix with lags and leads of an explanatory variable:

BJData <- cbind(as.data.frame(BJsales),as.data.frame(xregExpander(BJsales.lead,c(-7:7))))
colnames(BJData)[1] <- "y"
testModel <- adam(BJData, "ANN", h=18, silent=FALSE, holdout=TRUE, formula=y~xLag1+xLag2+xLag3)
testModel
#> Time elapsed: 0.13 seconds
#> Model estimated using adam() function: ETSX(ANN)
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 210.9197
#> Persistence vector g (excluding xreg):
#> alpha 
#>     1 
#> 
#> Sample size: 132
#> Number of estimated parameters: 6
#> Number of degrees of freedom: 126
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 433.8393 434.5113 451.1361 452.7768 
#> 
#> Forecast errors:
#> ME: 0.744; MAE: 1.299; RMSE: 1.782
#> sCE: 5.924%; Asymmetry: 44.2%; sMAE: 0.575%; sMSE: 0.006%
#> MASE: 1.065; RMSSE: 1.141; rMAE: 0.58; rRMSE: 0.71

Similarly to es(), there is a support for variables selection, but via the regressors parameter instead of xregDo, which will then use stepwise() function from greybox package on the residuals of the model:

testModel <- adam(BJData, "ANN", h=18, silent=FALSE, holdout=TRUE, regressors="select")

The same functionality is supported with ARIMA, so you can have, for example, ARIMAX(0,1,1), which is equivalent to ETSX(A,N,N):

testModel <- adam(BJData, "NNN", h=18, silent=FALSE, holdout=TRUE, regressors="select", orders=c(0,1,1))

The two models might differ because they have different initialisation in the optimiser and different bounds for parameters (ARIMA relies on invertibility condition, while ETS does the traditional (0,1) bounds by default). It is possible to make them identical if the number of iterations is increased and the initial parameters are the same. Here is an example of what happens, when the two models have exactly the same parameters:

BJData <- BJData[,c("y",names(testModel$initial$xreg))];
testModel <- adam(BJData, "NNN", h=18, silent=TRUE, holdout=TRUE, orders=c(0,1,1),
                  initial=testModel$initial, arma=testModel$arma)
testModel
#> Time elapsed: 0 seconds
#> Model estimated using adam() function: ARIMAX(0,1,1)
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 80.6624
#> ARMA parameters of the model:
#> MA:
#> theta1[1] 
#>    0.2379 
#> 
#> Sample size: 132
#> Number of estimated parameters: 1
#> Number of degrees of freedom: 131
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 163.3248 163.3555 166.2076 166.2827 
#> 
#> Forecast errors:
#> ME: 0.457; MAE: 0.571; RMSE: 0.687
#> sCE: 3.638%; Asymmetry: 82.1%; sMAE: 0.253%; sMSE: 0.001%
#> MASE: 0.468; RMSSE: 0.44; rMAE: 0.255; rRMSE: 0.274
names(testModel$initial)[1] <- names(testModel$initial)[[1]] <- "level"
testModel2 <- adam(BJData, "ANN", h=18, silent=TRUE, holdout=TRUE,
                   initial=testModel$initial, persistence=testModel$arma$ma+1)
testModel2
#> Time elapsed: 0 seconds
#> Model estimated using adam() function: ETSX(ANN)
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 1e+300
#> Persistence vector g (excluding xreg):
#>  alpha 
#> 1.2379 
#> 
#> Sample size: 132
#> Number of estimated parameters: 1
#> Number of degrees of freedom: 131
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 163.3248 163.3555 166.2076 166.2827 
#> 
#> Forecast errors:
#> ME: 0.457; MAE: 0.571; RMSE: 0.687
#> sCE: 3.638%; Asymmetry: 82.1%; sMAE: 0.253%; sMSE: 0.001%
#> MASE: 0.468; RMSSE: 0.44; rMAE: 0.255; rRMSE: 0.274

Another feature of ADAM is the time varying parameters in the SSOE framework, which can be switched on via regressors="adapt":

testModel <- adam(BJData, "ANN", h=18, silent=FALSE, holdout=TRUE, regressors="adapt")
testModel$persistence
#>        alpha       delta1       delta2       delta3       delta4       delta5 
#> 2.818516e-01 7.651576e-01 4.039971e-01 2.623775e-03 2.553495e-04 8.189351e-05

Note that the default number of iterations might not be sufficient in order to get close to the optimum of the function, so setting maxeval to something bigger might help. If you want to explore, why the optimisation stopped, you can provide print_level=41 parameter to the function, and it will print out the report from the optimiser. In the end, the default parameters are tuned in order to give a reasonable solution, but given the complexity of the model, they might not guarantee to give the best one all the time.

Finally, you can produce a mixture of ETS, ARIMA and regression, by using the respective parameters, like this:

testModel <- adam(BJData, "AAN", h=18, silent=FALSE, holdout=TRUE, orders=c(1,0,0))
summary(testModel)
#> 
#> Model estimated using adam() function: ETSX(AAN)+ARIMA(1,0,0)
#> Response variable: y
#> Distribution used in the estimation: Normal
#> Loss function type: likelihood; Loss function value: 74.487
#> Coefficients:
#>             Estimate Std. Error Lower 2.5% Upper 97.5%  
#> alpha         0.5579     0.1044     0.3512      0.7642 *
#> beta          0.0000     0.0147     0.0000      0.0291  
#> phi1[1]       0.7581     0.1384     0.4842      1.0000 *
#> level        42.1940     4.6637    32.9602     51.4122 *
#> trend         0.0752     0.0253     0.0251      0.1252 *
#> ARIMAState1   1.9223     1.3764    -0.8029      4.6429  
#> xLag3         5.0456     0.1245     4.7991      5.2917 *
#> xLag7         0.9462     0.1325     0.6838      1.2082 *
#> xLag4         4.1336     0.1557     3.8254      4.4413 *
#> xLag6         2.3273     0.1568     2.0169      2.6371 *
#> xLag5         3.1050     0.1598     2.7886      3.4210 *
#> 
#> Error standard deviation: 0.4462
#> Sample size: 132
#> Number of estimated parameters: 12
#> Number of degrees of freedom: 120
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 172.9740 175.5959 207.5677 213.9686

This might be handy, when you explore a high frequency data, want to add calendar events, apply ETS and add AR/MA errors to it.

Finally, if you estimate ETSX or ARIMAX model and want to speed things up, it is recommended to use initial="backcasting", which will then initialise dynamic part of the model via backcasting and use optimisation for the parameters of the explanatory variables:

testModel <- adam(BJData, "AAN", h=18, silent=TRUE, holdout=TRUE, initial="backcasting")
summary(testModel)
#> 
#> Model estimated using adam() function: ETSX(AAN)
#> Response variable: y
#> Distribution used in the estimation: Normal
#> Loss function type: likelihood; Loss function value: 62.3464
#> Coefficients:
#>       Estimate Std. Error Lower 2.5% Upper 97.5%  
#> alpha   1.0000     0.0871     0.8277      1.0000 *
#> beta    0.0047     0.0101     0.0000      0.0246  
#> xLag3   4.8015     3.3300    -1.7895     11.3852  
#> xLag7   0.7732     3.3408    -5.8392      7.3781  
#> xLag4   3.6139     3.0342    -2.3917      9.6128  
#> xLag6   1.6868     3.0303    -4.3111      7.6780  
#> xLag5   2.4306     2.8286    -3.1679      8.0229  
#> 
#> Error standard deviation: 0.4004
#> Sample size: 132
#> Number of estimated parameters: 8
#> Number of degrees of freedom: 124
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 140.6929 141.8636 163.7553 166.6135

Auto ADAM

While the original adam() function allows selecting ETS components and explanatory variables, it does not allow selecting the most suitable distribution and / or ARIMA components. This is what auto.adam() function is for.

In order to do the selection of the most appropriate distribution, you need to provide a vector of those that you want to check:

testModel <- auto.adam(BJsales, "XXX", silent=FALSE,
                       distribution=c("dnorm","dlaplace","ds"),
                       h=12, holdout=TRUE)
#> Evaluating models with different distributions... dnorm , dlaplace , ds , Done!
testModel
#> Time elapsed: 0.18 seconds
#> Model estimated using auto.adam() function: ETS(AAdN)
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 238.2715
#> Persistence vector g:
#>  alpha   beta 
#> 0.9534 0.2925 
#> Damping parameter: 0.8622
#> Sample size: 138
#> Number of estimated parameters: 6
#> Number of degrees of freedom: 132
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 488.5431 489.1843 506.1066 507.6863 
#> 
#> Forecast errors:
#> ME: 2.814; MAE: 2.969; RMSE: 3.655
#> sCE: 14.854%; Asymmetry: 87.8%; sMAE: 1.306%; sMSE: 0.026%
#> MASE: 2.492; RMSSE: 2.382; rMAE: 0.958; rRMSE: 0.954

This process can also be done in parallel on either the automatically selected number of cores (e.g. parallel=TRUE) or on the specified by user (e.g. parallel=4):

testModel <- auto.adam(BJsales, "ZZZ", silent=FALSE, parallel=TRUE,
                       h=12, holdout=TRUE)

If you want to add ARIMA or regression components, you can do it in the exactly the same way as for the adam() function. Here is an example of ETS+ARIMA:

testModel <- auto.adam(BJsales, "AAN", orders=list(ar=2,i=2,ma=2), silent=TRUE,
                       distribution=c("dnorm","dlaplace","ds","dgnorm"),
                       h=12, holdout=TRUE)
testModel
#> Time elapsed: 0.53 seconds
#> Model estimated using auto.adam() function: ETS(AAN)+ARIMA(2,2,2)
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 243.9582
#> Persistence vector g:
#>  alpha   beta 
#> 0.0143 0.0000 
#> 
#> ARMA parameters of the model:
#> AR:
#> phi1[1] phi2[1] 
#> -0.7434 -0.2804 
#> MA:
#> theta1[1] theta2[1] 
#>   -0.0786   -0.2418 
#> 
#> Sample size: 138
#> Number of estimated parameters: 13
#> Number of degrees of freedom: 125
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 513.9164 516.8519 551.9707 559.2026 
#> 
#> Forecast errors:
#> ME: 2.745; MAE: 2.901; RMSE: 3.554
#> sCE: 14.492%; Asymmetry: 87.9%; sMAE: 1.276%; sMSE: 0.024%
#> MASE: 2.435; RMSSE: 2.316; rMAE: 0.936; rRMSE: 0.927

However, this way the function will just use ARIMA(2,2,2) and fit it together with ETS. If you want it to select the most appropriate ARIMA orders from the provided (e.g. up to AR(2), I(1) and MA(2)), you need to add parameter select=TRUE to the list in orders:

testModel <- auto.adam(BJsales, "XXN", orders=list(ar=2,i=2,ma=2,select=TRUE),
                       distribution="default", silent=FALSE,
                       h=12, holdout=TRUE)
#> Evaluating models with different distributions... default ,  Selecting ARIMA orders... 
#> Selecting differences... 
#> Selecting ARMA... |
#> The best ARIMA is selected. Done!
testModel
#> Time elapsed: 0.16 seconds
#> Model estimated using auto.adam() function: ETS(AAdN) with drift
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 237.1294
#> Persistence vector g:
#>  alpha   beta 
#> 0.9541 0.2851 
#> Damping parameter: 0.8363
#> Sample size: 138
#> Number of estimated parameters: 7
#> Number of degrees of freedom: 131
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 488.2588 489.1204 508.7496 510.8721 
#> 
#> Forecast errors:
#> ME: 0.622; MAE: 1.2; RMSE: 1.5
#> sCE: 3.286%; Asymmetry: 48.9%; sMAE: 0.528%; sMSE: 0.004%
#> MASE: 1.007; RMSSE: 0.978; rMAE: 0.387; rRMSE: 0.391

Knowing how to work with adam(), you can use similar principles, when dealing with auto.adam(). Just keep in mind that the provided persistence, phi, initial, arma and B won’t work, because this contradicts the idea of the model selection.

Finally, there is also the mechanism of automatic outliers detection, which extracts residuals from the best model, flags observations that lie outside the prediction interval of thw width level in sample and then refits auto.adam() with the dummy variables for the outliers. Here how it works:

testModel <- auto.adam(AirPassengers, "PPP", silent=FALSE, outliers="use",
                       distribution="default",
                       h=12, holdout=TRUE)
#> Evaluating models with different distributions... default , 
#> Dealing with outliers...
testModel
#> Time elapsed: 1.92 seconds
#> Model estimated using auto.adam() function: ETSX(MMM)
#> Distribution assumed in the model: Gamma
#> Loss function type: likelihood; Loss function value: 464.5879
#> Persistence vector g (excluding xreg):
#>  alpha   beta  gamma 
#> 0.7633 0.0000 0.0000 
#> 
#> Sample size: 132
#> Number of estimated parameters: 18
#> Number of degrees of freedom: 114
#> Information criteria:
#>       AIC      AICc       BIC      BICc 
#>  965.1758  971.2289 1017.0663 1031.8443 
#> 
#> Forecast errors:
#> ME: -8.461; MAE: 15.346; RMSE: 22.431
#> sCE: -38.678%; Asymmetry: -49%; sMAE: 5.846%; sMSE: 0.73%
#> MASE: 0.637; RMSSE: 0.716; rMAE: 0.202; rRMSE: 0.218

If you specify outliers="select", the function will create leads and lags 1 of the outliers and then select the most appropriate ones via the regressors parameter of adam.

If you want to know more about ADAM, you are welcome to visit the online textbook (this is a work in progress at the moment).

Hyndman, Rob J, Anne B Koehler, J Keith Ord, and Ralph D Snyder. 2008. Forecasting with Exponential Smoothing. Springer Berlin Heidelberg.

Augmented Dynamic Adaptive Model

Ivan Svetunkov

2023-01-17

ADAM ETS

ADAM ARIMA

ADAM ETSX / ARIMAX / ETSX+ARIMA

Auto ADAM