## 5.6 Examples of application

### 5.6.1 Non-seasonal data

To see how the pure additive ADAM ETS works, we will try it out using the adam() function from the smooth package for R on Box-Jenkins sales data. We start with plotting the data:

plot(BJsales)

The series in Figure 5.1 seem to exhibit trend, so we will apply ETS(A,A,N) model:

adamETSBJ <- adam(BJsales, "AAN")
adamETSBJ
## Time elapsed: 0.02 seconds
## Model estimated using adam() function: ETS(AAN)
## Distribution assumed in the model: Normal
## Loss function type: likelihood; Loss function value: 258.8098
## Persistence vector g:
##  alpha   beta
## 1.0000 0.2438
##
## Sample size: 150
## Number of estimated parameters: 5
## Number of degrees of freedom: 145
## Information criteria:
##      AIC     AICc      BIC     BICc
## 527.6196 528.0362 542.6728 543.7166

The model’s output summarises which specific model was constructed, assuming what distribution, how it was estimated, and the values of smoothing parameters. It also reports the sample size, the number of parameters, degrees of freedom and produces information criteria (see Section 13.4 of I. Svetunkov, 2022a). We can compare this model with the ETS(A,N,N) to see which of them performs better in terms of information criteria (e.g. in terms of AICc):

adam(BJsales, "ANN")
## Time elapsed: 0.01 seconds
## Model estimated using adam() function: ETS(ANN)
## Distribution assumed in the model: Normal
## Loss function type: likelihood; Loss function value: 273.2898
## Persistence vector g:
## alpha
##     1
##
## Sample size: 150
## Number of estimated parameters: 3
## Number of degrees of freedom: 147
## Information criteria:
##      AIC     AICc      BIC     BICc
## 552.5795 552.7439 561.6114 562.0233

In this situation the AICc for ETS(A,N,N) is higher than for ETS(A,A,N), so we should use the latter for forecasting purposes. We can produce point forecasts and prediction interval (in this example we will construct 90% and 95% ones) and plot them (Figure 5.2):

plot(forecast(adamETSBJ, h=10,
interval="prediction", level=c(0.9,0.95)),
main="")

Notice that the smoothing parameters of ETS(A,A,N) are very high, with $$\alpha=1$$. This might mean that the maximum likelihood is achieved in the admissible bounds. We can try it out and see what happens:

adamETSBJ <- adam(BJsales, "AAN", bounds="admissible")
adamETSBJ
## Time elapsed: 0.03 seconds
## Model estimated using adam() function: ETS(AAN)
## Distribution assumed in the model: Normal
## Loss function type: likelihood; Loss function value: 258.5358
## Persistence vector g:
##  alpha   beta
## 1.0541 0.2185
##
## Sample size: 150
## Number of estimated parameters: 5
## Number of degrees of freedom: 145
## Information criteria:
##      AIC     AICc      BIC     BICc
## 527.0716 527.4883 542.1248 543.1687

Both smoothing parameters are now higher, which implies that the uncertainty about the future values of states is higher as well, which is then reflected in the slightly wider prediction interval (Figure 5.3):

plot(forecast(adamETSBJ, h=10,
interval="prediction", level=c(0.9,0.95)))

Although the values of smoothing parameters are larger than one, the model is still stable. In order to see that, we can calculate the discount matrix $$\mathbf{D}$$ using the objects returned by the function:

discountMatrix <- adamETSBJ$transition - adamETSBJ$persistence %*%
adamETSBJ$measurement[nobs(adamETSBJ),,drop=FALSE] eigen(discountMatrix)$values
## [1]  0.79538429 -0.06800887

Notice that the absolute values of both eigenvalues in the matrix are less than one, which means that the newer observations have higher weights than the older ones and that the absolute values of weights decrease over time, making the model stable.

If we want to test ADAM ETS with another distribution, it can be done using the respective parameter (here we use Generalised Normal, estimating the shape together with the other parameters):

adamETSBJ <- adam(BJsales, "AAN", distribution="dgnorm")
print(adamETSBJ, digits=3)
## Time elapsed: 0.03 seconds
## Model estimated using adam() function: ETS(AAN)
## Distribution assumed in the model: Generalised Normal with shape=1.741
## Loss function type: likelihood; Loss function value: 258.456
## Persistence vector g:
## alpha  beta
## 1.000 0.217
##
## Sample size: 150
## Number of estimated parameters: 6
## Number of degrees of freedom: 144
## Information criteria:
##     AIC    AICc     BIC    BICc
## 528.913 529.500 546.977 548.448

Similar to the previous cases, we can plot the forecasts from the model:

plot(forecast(adamETSBJ, h=10, interval="prediction"),
main="")

The prediction interval in this case is slightly wider than in the previous one, because $$\mathcal{GN}$$ distribution with $$\beta=$$ 1.74 has fatter tails than the normal distribution (Figure 5.4).

### 5.6.2 Seasonal data

Now we will check what happens in the case of seasonal data. We use AirPassengers data, plotted in Figure 5.5, which has multiplicative seasonality. But for demonstration purposes, we will see what happens when we use the wrong model with additive seasonality. We will withhold 12 observations to look closer at the performance of the ETS(A,A,A) model in this case:

adamETSAir <- adam(AirPassengers, "AAA", lags=12,
h=12, holdout=TRUE)

Remark. In this specific case, the lags parameter is not necessary because the function will automatically get the frequency from the ts object. If we were to provide a vector of values instead of the ts object, we would need to specify the correct lag. Note that 1 (lag for level and trend) is unnecessary; the function will always use it anyway.

Remark. In some cases, the optimiser might converge to the local minimum, so if you find the results unsatisfactory, it might make sense to reestimate the model tuning the parameters of the optimiser (see Section 11.4 for details). Here is an example (we increase the number of iterations in the optimisation and set new starting values for the smoothing parameters):

adamETSAir$B[1:3] <- c(0.2,0.1,0.3) adamETSAir <- adam(AirPassengers, "AAA", lags=12, h=12, holdout=TRUE, B=adamETSAir$B, maxeval=1000)
adamETSAir
## Time elapsed: 0.11 seconds
## Model estimated using adam() function: ETS(AAA)
## Distribution assumed in the model: Normal
## Loss function type: likelihood; Loss function value: 513.0026
## Persistence vector g:
##  alpha   beta  gamma
## 0.1928 0.0000 0.8072
##
## Sample size: 132
## Number of estimated parameters: 17
## Number of degrees of freedom: 115
## Information criteria:
##      AIC     AICc      BIC     BICc
## 1060.005 1065.374 1109.013 1122.119
##
## Forecast errors:
## ME: 6.216; MAE: 14.162; RMSE: 17.75
## sCE: 28.418%; Asymmetry: 51.9%; sMAE: 5.395%; sMSE: 0.457%
## MASE: 0.588; RMSSE: 0.567; rMAE: 0.186; rRMSE: 0.172

Notice that because we fit the seasonal additive model to the data with multiplicative seasonality, the smoothing parameter $$\gamma$$ has become large – the seasonal component needs to be updated to keep up with the changing seasonal profile. In addition, because we use the holdout parameter, the function also reports the error measures for the point forecasts on that part of the data. This can be useful when comparing the performance of several models on a time series. Here is how the forecast from ETS(A,A,A) looks on this data:

Figure 5.6 demonstrates that while the fit to the data is far from perfect, due to a pure coincidence, the point forecast from this model is decent.

In order to see how the ADAM ETS decomposes the data into components, we can plot it via the plot() method with which parameter:

We can see on the graph in Figure 5.7 that the residuals still contain some seasonality, so there is room for improvement. This probably happened because the data exhibits multiplicative seasonality rather than the additive one. For now, we do not aim to fix this issue.

### References

• Svetunkov, I., 2022a. Statistics for business analytics. https://openforecast.org/sba/ (version: 31.03.2022)