# es() - Exponential Smoothing

#### Ivan Svetunkov

#### 2026-06-20

`es()` is a part of smooth package and is a wrapper for the ADAM function with `distribution="dnorm"`. It implements Exponential Smoothing in the ETS form, selecting the most appropriate model among 30 possible ones.

We will use some of the functions of the `greybox` package in this vignette for demonstrational purposes.

Let’s load the necessary packages:

    require(smooth)
    require(greybox)

The simplest call for the `es()` function is:

    ourModel <- es(BJsales, h=12, holdout=TRUE, silent=FALSE)

    ## Forming the pool of models based on... ANN , AAN , Estimation progress:    60 %80 %100 %... Done!

    ourModel

    ## Time elapsed: 0.08 seconds
    ## Model estimated using es() function: ETS(AAdN)
    ## With backcasting initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 237.6128
    ## Persistence vector g:
    ##  alpha   beta
    ## 0.9448 0.2979
    ## Damping parameter: 0.8789
    ## Sample size: 138
    ## Number of estimated parameters: 4
    ## Number of degrees of freedom: 134
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 483.2257 483.5264 494.9347 495.6756
    ##
    ## Forecast errors:
    ## ME: 2.817; MAE: 2.967; RMSE: 3.654
    ## sCE: 14.869%; Asymmetry: 88%; sMAE: 1.305%; sMSE: 0.026%
    ## MASE: 2.491; RMSSE: 2.382; rMAE: 0.957; rRMSE: 0.954

In this case function uses branch and bound algorithm to form a pool of models to check and after that constructs a model with the lowest information criterion. As we can see, it also produces an output with brief information about the model, which contains:

1. How much time was elapsed for the model construction;
2. What type of ETS was selected;
3. Values of persistence vector (smoothing parameters);
4. What type of initialisation was used;
5. How many parameters were estimated (standard deviation is included);
6. Cost function type and the value of that cost function;
7. Information criteria for this model;
8. Forecast errors (because we have set `holdout=TRUE`).

The function has also produced a graph with actual values, fitted values and point forecasts.

If we need prediction interval, then we can use the `forecast()` method:

    plot(forecast(ourModel, h=12, interval="prediction"))

The same model can be reused for different purposes, for example to produce forecasts based on newly available data:

    es(BJsales, model=ourModel, h=12, holdout=FALSE)

    ## Time elapsed: 0 seconds
    ## Model estimated using es() function: ETS(AAdN)
    ## With provided initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 259.7882
    ## Persistence vector g:
    ##  alpha   beta
    ## 0.9448 0.2979
    ## Damping parameter: 0.8789
    ## Sample size: 150
    ## Number of estimated parameters: 1
    ## Number of degrees of freedom: 149
    ## Number of provided parameters: 5
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 521.5763 521.6034 524.5870 524.6547

We can also extract the type of model in order to reuse it later:

    modelType(ourModel)

    ## [1] "AAdN"

This handy function also works with `ets()` from forecast package.

If we need actual values from the model, we can use `actuals()` method from `greybox` package:

    actuals(ourModel)

    ## Time Series:
    ## Start = 1
    ## End = 138
    ## Frequency = 1
    ##   [1] 200.1 199.5 199.4 198.9 199.0 200.2 198.6 200.0 200.3 201.2 201.6 201.5
    ##  [13] 201.5 203.5 204.9 207.1 210.5 210.5 209.8 208.8 209.5 213.2 213.7 215.1
    ##  [25] 218.7 219.8 220.5 223.8 222.8 223.8 221.7 222.3 220.8 219.4 220.1 220.6
    ##  [37] 218.9 217.8 217.7 215.0 215.3 215.9 216.7 216.7 217.7 218.7 222.9 224.9
    ##  [49] 222.2 220.7 220.0 218.7 217.0 215.9 215.8 214.1 212.3 213.9 214.6 213.6
    ##  [61] 212.1 211.4 213.1 212.9 213.3 211.5 212.3 213.0 211.0 210.7 210.1 211.4
    ##  [73] 210.0 209.7 208.8 208.8 208.8 210.6 211.9 212.8 212.5 214.8 215.3 217.5
    ##  [85] 218.8 220.7 222.2 226.7 228.4 233.2 235.7 237.1 240.6 243.8 245.3 246.0
    ##  [97] 246.3 247.7 247.6 247.8 249.4 249.0 249.9 250.5 251.5 249.0 247.6 248.8
    ## [109] 250.4 250.7 253.0 253.7 255.0 256.2 256.0 257.4 260.4 260.0 261.3 260.4
    ## [121] 261.6 260.8 259.8 259.0 258.9 257.4 257.7 257.9 257.4 257.3 257.6 258.9
    ## [133] 257.8 257.7 257.2 257.5 256.8 257.5

We can also use persistence or initials only from the model to construct the other one:

    # Provided initials
    es(BJsales, model=modelType(ourModel),
       h=12, holdout=FALSE,
       initial=ourModel$initial)

    ## Time elapsed: 0.02 seconds
    ## Model estimated using es() function: ETS(AAdN)
    ## With provided initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 259.7412
    ## Persistence vector g:
    ##  alpha   beta
    ## 0.9674 0.2741
    ## Damping parameter: 0.8787
    ## Sample size: 150
    ## Number of estimated parameters: 4
    ## Number of degrees of freedom: 146
    ## Number of provided parameters: 2
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 527.4824 527.7582 539.5249 540.2160

    # Provided persistence
    es(BJsales, model=modelType(ourModel),
       h=12, holdout=FALSE,
       persistence=ourModel$persistence)

    ## Time elapsed: 0.01 seconds
    ## Model estimated using es() function: ETS(AAdN)
    ## With backcasting initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 255.3469
    ## Persistence vector g:
    ##  alpha   beta
    ## 0.9448 0.2979
    ## Damping parameter: 0.8737
    ## Sample size: 150
    ## Number of estimated parameters: 2
    ## Number of degrees of freedom: 148
    ## Number of provided parameters: 2
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 514.6938 514.7754 520.7150 520.9195

or provide some arbitrary values:

    es(BJsales, model=modelType(ourModel),
       h=12, holdout=FALSE,
       initial=200)

    ## Time elapsed: 0.03 seconds
    ## Model estimated using es() function: ETS(AAdN)
    ## With provided initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 255.4015
    ## Persistence vector g:
    ##  alpha   beta
    ## 1.0000 0.2709
    ## Damping parameter: 0.8854
    ## Sample size: 150
    ## Number of estimated parameters: 5
    ## Number of degrees of freedom: 145
    ## Number of provided parameters: 1
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 520.8031 521.2198 535.8563 536.9002

Using some other parameters may lead to completely different model and forecasts (see discussion of the additional parameters in the online textbook about ADAM):

    es(BJsales, h=12, holdout=TRUE, loss="MSEh", bounds="a", ic="BIC")

    ## Time elapsed: 0.14 seconds
    ## Model estimated using es() function: ETS(AAN)
    ## With backcasting initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: MSEh; Loss function value: 78.4964
    ## Persistence vector g:
    ## alpha  beta
    ## 1.544 0.000
    ##
    ## Sample size: 138
    ## Number of estimated parameters: 2
    ## Number of degrees of freedom: 136
    ## Information criteria:
    ##       AIC      AICc       BIC      BICc
    ##  997.7283  997.8172 1003.5828 1003.8018
    ##
    ## Forecast errors:
    ## ME: -0.492; MAE: 1.226; RMSE: 1.371
    ## sCE: -2.596%; Asymmetry: -45.6%; sMAE: 0.539%; sMSE: 0.004%
    ## MASE: 1.029; RMSSE: 0.894; rMAE: 0.396; rRMSE: 0.358

You can play around with all the available parameters to see what’s their effect on the final model.

In order to combine forecasts we need to use “C” letter:

    es(BJsales, model="CCN", h=12, holdout=TRUE)

    ## Time elapsed: 0.2 seconds
    ## Model estimated: ETS(CCN)
    ## Loss function type: likelihood
    ##
    ## Number of models combined: 10
    ## Sample size: 138
    ## Average number of estimated parameters: 3.89
    ## Average number of degrees of freedom: 134.11
    ##
    ## Forecast errors:
    ## ME: 2.833; MAE: 2.98; RMSE: 3.671
    ## sCE: 14.954%; sMAE: 1.311%; sMSE: 0.026%
    ## MASE: 2.502; RMSSE: 2.393; rMAE: 0.961; rRMSE: 0.958

Model selection from a specified pool and forecasts combination are called using respectively:

    # Select the best model in the pool
    es(BJsales, model=c("ANN","AAN","AAdN","MNN","MAN","MAdN"),
       h=12, holdout=TRUE)

    ## Time elapsed: 0.08 seconds
    ## Model estimated using es() function: ETS(AAdN)
    ## With backcasting initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 237.6128
    ## Persistence vector g:
    ##  alpha   beta
    ## 0.9448 0.2979
    ## Damping parameter: 0.8789
    ## Sample size: 138
    ## Number of estimated parameters: 4
    ## Number of degrees of freedom: 134
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 483.2257 483.5264 494.9347 495.6756
    ##
    ## Forecast errors:
    ## ME: 2.817; MAE: 2.967; RMSE: 3.654
    ## sCE: 14.869%; Asymmetry: 88%; sMAE: 1.305%; sMSE: 0.026%
    ## MASE: 2.491; RMSSE: 2.382; rMAE: 0.957; rRMSE: 0.954

    # Combine the pool of models
    es(BJsales, model=c("CCC","ANN","AAN","AAdN","MNN","MAN","MAdN"),
       h=12, holdout=TRUE)

    ## Time elapsed: 0.08 seconds
    ## Model estimated: ETS(CCN)
    ## Loss function type: likelihood
    ##
    ## Number of models combined: 6
    ## Sample size: 138
    ## Average number of estimated parameters: 3.8643
    ## Average number of degrees of freedom: 134.1357
    ##
    ## Forecast errors:
    ## ME: 2.837; MAE: 2.983; RMSE: 3.675
    ## sCE: 14.974%; sMAE: 1.312%; sMSE: 0.026%
    ## MASE: 2.504; RMSSE: 2.396; rMAE: 0.962; rRMSE: 0.959

Now we introduce explanatory variable in ETS:

    x <- BJsales.lead

and fit an ETSX model with the exogenous variable first:

    es(BJsales, model="ZZZ", h=12, holdout=TRUE,
       xreg=x)

    ## Time elapsed: 0.55 seconds
    ## Model estimated using es() function: ETSX(AMdN)
    ## With backcasting initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 237.5066
    ## Persistence vector g (excluding xreg):
    ##  alpha   beta
    ## 0.9505 0.2902
    ## Damping parameter: 0.8773
    ## Sample size: 138
    ## Number of estimated parameters: 5
    ## Number of degrees of freedom: 133
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 485.0132 485.4677 499.6494 500.7693
    ##
    ## Forecast errors:
    ## ME: 2.876; MAE: 3; RMSE: 3.702
    ## sCE: 15.183%; Asymmetry: 90%; sMAE: 1.319%; sMSE: 0.027%
    ## MASE: 2.518; RMSSE: 2.413; rMAE: 0.968; rRMSE: 0.966

If we want to check if lagged x can be used for forecasting purposes, we can use `xregExpander()` function from `greybox` package:

    es(BJsales, model="ZZZ", h=12, holdout=TRUE,
       xreg=xregExpander(x), regressors="use")

    ## Time elapsed: 1.65 seconds
    ## Model estimated using es() function: ETSX(AMdN)
    ## With backcasting initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 236.4494
    ## Persistence vector g (excluding xreg):
    ##  alpha   beta
    ## 1.0000 0.3144
    ## Damping parameter: 0.8373
    ## Sample size: 138
    ## Number of estimated parameters: 7
    ## Number of degrees of freedom: 131
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 486.8989 487.7604 507.3897 509.5122
    ##
    ## Forecast errors:
    ## ME: 2.346; MAE: 2.847; RMSE: 3.345
    ## sCE: 12.385%; Asymmetry: 72.4%; sMAE: 1.253%; sMSE: 0.022%
    ## MASE: 2.39; RMSSE: 2.181; rMAE: 0.918; rRMSE: 0.873

We can also construct a model with selected exogenous (based on IC):

    es(BJsales, model="ZZZ", h=12, holdout=TRUE,
       xreg=xregExpander(x), regressors="select")

    ## Time elapsed: 1.18 seconds
    ## Model estimated using es() function: ETS(AMdN)
    ## With backcasting initialisation
    ## Distribution assumed in the model: Normal
    ## Loss function type: likelihood; Loss function value: 237.5549
    ## Persistence vector g:
    ##  alpha   beta
    ## 0.9443 0.2959
    ## Damping parameter: 0.8733
    ## Sample size: 138
    ## Number of estimated parameters: 4
    ## Number of degrees of freedom: 134
    ## Information criteria:
    ##      AIC     AICc      BIC     BICc
    ## 483.1098 483.4106 494.8188 495.5598
    ##
    ## Forecast errors:
    ## ME: 2.819; MAE: 2.969; RMSE: 3.656
    ## sCE: 14.879%; Asymmetry: 88%; sMAE: 1.306%; sMSE: 0.026%
    ## MASE: 2.492; RMSSE: 2.383; rMAE: 0.958; rRMSE: 0.954

Finally, if you work with M or M3 data, and need to test a function on a specific time series, you can use the following simplified call:

    es(Mcomp::M3$N2457, silent=FALSE)

This command has taken the data, split it into in-sample and holdout and produced the forecast of appropriate length to the holdout.