9.7 ARIMA modelling in
ARIMA() function in the
fable package uses a variation of the Hyndman-Khandakar algorithm (Hyndman & Khandakar, 2008), which combines unit root tests, minimisation of the AICc and MLE to obtain an ARIMA model. The arguments to
ARIMA() provide for many variations on the algorithm. What is described here is the default behaviour.
|Hyndman-Khandakar algorithm for automatic ARIMA modelling|
Figure 9.11 illustrates diagrammatically how the Hyndman-Khandakar algorithm traverses the space of the ARMA orders, through an example. The grid covers combinations of ARMA(\(p,q\)) orders starting from the top-left corner with an ARMA(\(0,0\)), with the AR order increasing down the vertical axis, and the MA order increasing across the horizontal axis.
The orange cells show the initial set of models considered by the algorithm. In this example, the ARMA(2,2) model has the lowest AICc value amongst these models. This is called the “current model” and is shown by the black circle. The algorithm then searches over neighbouring models as shown by the blue arrows. If a better model is found then this becomes the new “current model.” In this example, the new “current model” is the ARMA(3,3) model. The algorithm continues in this fashion until no better model can be found. In this example the model returned is an ARMA(4,2) model.
The default procedure uses some approximations to speed up the search. These approximations can be avoided with the argument
approximation=FALSE. It is possible that the minimum AICc model will not be found due to these approximations, or because of the use of a stepwise procedure. A much larger set of models will be searched if the argument
stepwise=FALSE is used. See the help file for a full description of the arguments.
When fitting an ARIMA model to a set of (non-seasonal) time series data, the following procedure provides a useful general approach.
- Plot the data and identify any unusual observations.
- If necessary, transform the data (using a Box-Cox transformation) to stabilise the variance.
- If the data are non-stationary, take first differences of the data until the data are stationary.
- Examine the ACF/PACF: Is an ARIMA(\(p,d,0\)) or ARIMA(\(0,d,q\)) model appropriate?
- Try your chosen model(s), and use the AICc to search for a better model.
- Check the residuals from your chosen model by plotting the ACF of the residuals, and doing a portmanteau test of the residuals. If they do not look like white noise, try a modified model.
- Once the residuals look like white noise, calculate forecasts.
The Hyndman-Khandakar algorithm only takes care of steps 3–5. So even if you use it, you will still need to take care of the other steps yourself.
The process is summarised in Figure 9.12.
Example: Central African Republic exports
We will apply this procedure to the exports of the Central African Republic shown in Figure 9.13.
%>% global_economy filter(Code == "CAF") %>% autoplot(Exports) + labs(title="Central African Republic exports", y="% of GDP")
The time plot shows some non-stationarity, with an overall decline. The improvement in 1994 was due to a new government which overthrew the military junta and had some initial success, before unrest caused further economic decline.
There is no evidence of changing variance, so we will not do a Box-Cox transformation.
To address the non-stationarity, we will take a first difference of the data. The differenced data are shown in Figure 9.14.
%>% global_economy filter(Code == "CAF") %>% gg_tsdisplay(difference(Exports), plot_type='partial')
These now appear to be stationary.
The PACF shown in Figure 9.14 is suggestive of an AR(2) model; so an initial candidate model is an ARIMA(2,1,0). The ACF suggests an MA(3) model; so an alternative candidate is an ARIMA(0,1,3).
We fit both an ARIMA(2,1,0) and an ARIMA(0,1,3) model along with two automated model selections, one using the default stepwise procedure, and one working harder to search a larger model space.
<- global_economy %>% caf_fit filter(Code == "CAF") %>% model(arima210 = ARIMA(Exports ~ pdq(2,1,0)), arima013 = ARIMA(Exports ~ pdq(0,1,3)), stepwise = ARIMA(Exports), search = ARIMA(Exports, stepwise=FALSE)) %>% pivot_longer(!Country, names_to = "Model name", caf_fit values_to = "Orders") #> # A mable: 4 x 3 #> # Key: Country, Model name  #> Country `Model name` Orders #> <fct> <chr> <model> #> 1 Central African Republic arima210 <ARIMA(2,1,0)> #> 2 Central African Republic arima013 <ARIMA(0,1,3)> #> 3 Central African Republic stepwise <ARIMA(2,1,2)> #> 4 Central African Republic search <ARIMA(3,1,0)> glance(caf_fit) %>% arrange(AICc) %>% select(.model:BIC) #> # A tibble: 4 x 6 #> .model sigma2 log_lik AIC AICc BIC #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 search 6.52 -133. 274. 275. 282. #> 2 arima210 6.71 -134. 275. 275. 281. #> 3 arima013 6.54 -133. 274. 275. 282. #> 4 stepwise 6.42 -132. 274. 275. 284.
The four models have almost identical AICc values. Of the models fitted, the full search has found that an ARIMA(3,1,0) gives the lowest AICc value, closely followed by the ARIMA(2,1,0) and ARIMA(0,1,3) — the latter two being the models that we guessed from the ACF and PACF plots. The automated stepwise selection has identified an ARIMA(2,1,2) model, which has the highest AICc value of the four models.
The ACF plot of the residuals from the ARIMA(3,1,0) model shows that all autocorrelations are within the threshold limits, indicating that the residuals are behaving like white noise.
%>% caf_fit select(search) %>% gg_tsresiduals()
A portmanteau test returns a large p-value, also suggesting that the residuals are white noise.
augment(caf_fit) %>% filter(.model=='search') %>% features(.innov, ljung_box, lag = 10, dof = 3) #> # A tibble: 1 x 4 #> Country .model lb_stat lb_pvalue #> <fct> <chr> <dbl> <dbl> #> 1 Central African Republic search 5.75 0.569
Forecasts from the chosen model are shown in Figure 9.16.
%>% caf_fit forecast(h=5) %>% filter(.model=='search') %>% autoplot(global_economy)
Note that the mean forecasts look very similar to what we would get with a random walk (equivalent to an ARIMA(0,1,0)). The extra work to include AR and MA terms has made little difference to the point forecasts in this example, although the prediction intervals are much narrower than for a random walk model.
Understanding constants in R
A non-seasonal ARIMA model can be written as
(1-\phi_1B - \cdots - \phi_p B^p)(1-B)^d y_t = c + (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t,
or equivalently as
(1-\phi_1B - \cdots - \phi_p B^p)(1-B)^d (y_t - \mu t^d/d!) = (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t,
where \(c = \mu(1-\phi_1 - \cdots - \phi_p )\) and \(\mu\) is the mean of \((1-B)^d y_t\). The
fable package uses the parameterisation of Equation (9.3) and most other R implementations use Equation (9.4).
Thus, the inclusion of a constant in a non-stationary ARIMA model is equivalent to inducing a polynomial trend of order \(d\) in the
forecast() function. (If the constant is omitted, the
forecast() function includes a polynomial trend of order \(d-1\).) When \(d=0\), we have the special case that \(\mu\) is the mean of \(y_t\).
By default, the
ARIMA() function will automatically determine if a constant should be included. For \(d=0\) or \(d=1\), a constant will be included if it improves the AICc value. If \(d>1\) the constant is always omitted as a quadratic or higher order trend is particularly dangerous when forecasting.
The constant can be specified by including
1 in the model formula (like the intercept in
lm()). For example, to automatically select an ARIMA model with a constant, you could use
ARIMA(y ~ 1 + ...). Similarly, a constant can be excluded with
ARIMA(y ~ 0 + ...).
Plotting the characteristic roots
(This is a more advanced section and can be skipped if desired.)
We can re-write Equation (9.3) as \[\phi(B) (1-B)^d y_t = c + \theta(B) \varepsilon_t\] where \(\phi(B)= (1-\phi_1B - \cdots - \phi_p B^p)\) is a \(p\)th order polynomial in \(B\) and \(\theta(B) = (1 + \theta_1 B + \cdots + \theta_q B^q)\) is a \(q\)th order polynomial in \(B\).
The stationarity conditions for the model are that the \(p\) complex roots of \(\phi(B)\) lie outside the unit circle, and the invertibility conditions are that the \(q\) complex roots of \(\theta(B)\) lie outside the unit circle. So we can see whether the model is close to invertibility or stationarity by a plot of the roots in relation to the complex unit circle.
It is easier to plot the inverse roots instead, as they should all lie within the unit circle. This is easily done in R. For the ARIMA(3,1,0) model fitted to the Central African Republic Exports, we obtain Figure 9.17.
gg_arma(caf_fit %>% select(Country, search))
The three orange dots in the plot correspond to the roots of the polynomials \(\phi(B)\). They are all inside the unit circle, as we would expect because
fable ensures the fitted model is both stationary and invertible. Any roots close to the unit circle may be numerically unstable, and the corresponding model will not be good for forecasting.
ARIMA() function will never return a model with inverse roots outside the unit circle. Models automatically selected by the
ARIMA() function will not contain roots close to the unit circle either. Consequently, it is sometimes possible to find a model with better AICc value than
ARIMA() will return, but such models will be potentially problematic.