## 9.5 Non-seasonal ARIMA models

If we combine differencing with autoregression and a moving average model, we obtain a non-seasonal ARIMA model. ARIMA is an acronym for AutoRegressive Integrated Moving Average (in this context, “integration” is the reverse of differencing). The full model can be written as
\[\begin{equation}
y'_{t} = c + \phi_{1}y'_{t-1} + \cdots + \phi_{p}y'_{t-p}
+ \theta_{1}\varepsilon_{t-1} + \cdots + \theta_{q}\varepsilon_{t-q} + \varepsilon_{t}, \tag{9.1}
\end{equation}\]
where \(y'_{t}\) is the differenced series (it may have been differenced more than once). The “predictors” on the right hand side include both lagged values of \(y_t\) and lagged errors. We call this an **ARIMA(\(p, d, q\)) model**, where

\(p=\) | order of the autoregressive part; |

\(d=\) | degree of first differencing involved; |

\(q=\) | order of the moving average part. |

The same stationarity and invertibility conditions that are used for autoregressive and moving average models also apply to an ARIMA model.

Many of the models we have already discussed are special cases of the ARIMA model, as shown in Table 9.1.

White noise | ARIMA(0,0,0) |

Random walk | ARIMA(0,1,0) with no constant |

Random walk with drift | ARIMA(0,1,0) with a constant |

Autoregression | ARIMA(\(p\),0,0) |

Moving average | ARIMA(0,0,\(q\)) |

Once we start combining components in this way to form more complicated models, it is much easier to work with the backshift notation. For example, Equation (9.1) can be written in backshift notation as \[\begin{equation} \tag{9.2} \begin{array}{c c c c} (1-\phi_1B - \cdots - \phi_p B^p) & (1-B)^d y_{t} &= &c + (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t\\ {\uparrow} & {\uparrow} & &{\uparrow}\\ \text{AR($p$)} & \text{$d$ differences} & & \text{MA($q$)}\\ \end{array} \end{equation}\]

Selecting appropriate values for \(p\), \(d\) and \(q\) can be difficult. However, the `ARIMA()`

function in R will do it for you automatically. In Section 9.7, we will learn how this function works, along with some methods for choosing these values yourself.

### US consumption expenditure

Figure 9.7 shows quarterly percentage changes in US consumption expenditure. Although it is a quarterly series, there does not appear to be a seasonal pattern, so we will fit a non-seasonal ARIMA model (by setting `PDQ(0,0,0)`

).

```
%>% autoplot(Consumption) +
us_change labs(x = "Year", y = "Quarterly percentage change", title = "US consumption")
```

The following R code was used to select a model automatically.

```
us_change %>%
fit <- model(ARIMA(Consumption ~ PDQ(0,0,0)))
report(fit)
#> Series: Consumption
#> Model: ARIMA(1,0,3) w/ mean
#>
#> Coefficients:
#> ar1 ma1 ma2 ma3 constant
#> 0.5731 -0.3617 0.0925 0.1934 0.3160
#> s.e. 0.1503 0.1607 0.0787 0.0824 0.0371
#>
#> sigma^2 estimated as 0.3334: log likelihood=-169.9
#> AIC=351.8 AICc=352.2 BIC=371.5
```

This is an ARIMA(1,0,3) model: \[ y_t = 0.316 + 0.573y_{t-1} -0.362 \varepsilon_{t-1} + 0.0925 \varepsilon_{t-2} + 0.193 \varepsilon_{t-3} + \varepsilon_{t}, \] where \(\varepsilon_t\) is white noise with a standard deviation of \(0.577 = \sqrt{0.333}\). Forecasts from the model are shown in Figure 9.8.

`%>% forecast(h=10) %>% autoplot(slice(us_change, (n()-80):n())) fit `

### Understanding ARIMA models

The `ARIMA()`

function is useful, but anything automated can be a little dangerous, and it is worth understanding something of the behaviour of the models even when you rely on an automatic procedure to choose the model for you.

The constant \(c\) has an important effect on the long-term forecasts obtained from these models.

- If \(c=0\) and \(d=0\), the long-term forecasts will go to zero.
- If \(c=0\) and \(d=1\), the long-term forecasts will go to a non-zero constant.
- If \(c=0\) and \(d=2\), the long-term forecasts will follow a straight line.
- If \(c\ne0\) and \(d=0\), the long-term forecasts will go to the mean of the data.
- If \(c\ne0\) and \(d=1\), the long-term forecasts will follow a straight line.
- If \(c\ne0\) and \(d=2\), the long-term forecasts will follow a quadratic trend.

The value of \(d\) also has an effect on the prediction intervals — the higher the value of \(d\), the more rapidly the prediction intervals increase in size. For \(d=0\), the long-term forecast standard deviation will go to the standard deviation of the historical data, so the prediction intervals will all be essentially the same.

This behaviour is seen in Figure 9.8 where \(d=0\) and \(c\ne0\). In this figure, the prediction intervals are almost the same for the last few forecast horizons, and the point forecasts are equal to the mean of the data.

The value of \(p\) is important if the data show cycles. To obtain cyclic forecasts, it is necessary to have \(p\ge2\), along with some additional conditions on the parameters. For an AR(2) model, cyclic behaviour occurs if \(\phi_1^2+4\phi_2<0\). In that case, the average period of the cycles is^{15}
\[
\frac{2\pi}{\text{arc cos}(-\phi_1(1-\phi_2)/(4\phi_2))}.
\]

### ACF and PACF plots

It is usually not possible to tell, simply from a time plot, what values of \(p\) and \(q\) are appropriate for the data. However, it is sometimes possible to use the ACF plot, and the closely related PACF plot, to determine appropriate values for \(p\) and \(q\).

Recall that an ACF plot shows the autocorrelations which measure the relationship between \(y_t\) and \(y_{t-k}\) for different values of \(k\). Now if \(y_t\) and \(y_{t-1}\) are correlated, then \(y_{t-1}\) and \(y_{t-2}\) must also be correlated. However, then \(y_t\) and \(y_{t-2}\) might be correlated, simply because they are both connected to \(y_{t-1}\), rather than because of any new information contained in \(y_{t-2}\) that could be used in forecasting \(y_t\).

To overcome this problem, we can use **partial autocorrelations**. These measure the relationship between \(y_{t}\) and \(y_{t-k}\) after removing the effects of lags \(1, 2, 3, \dots, k - 1\). So the first partial autocorrelation is identical to the first autocorrelation, because there is nothing between them to remove. Each partial autocorrelation can be estimated as the last coefficient in an autoregressive model. Specifically, \(\alpha_k\), the \(k\)th partial autocorrelation coefficient, is equal to the estimate of \(\phi_k\) in an AR(\(k\)) model. In practice, there are more efficient algorithms for computing \(\alpha_k\) than fitting all of these autoregressions, but they give the same results.

Figures 9.9 and 9.10 shows the ACF and PACF plots for the US consumption data shown in Figure 9.7. The partial autocorrelations have the same critical values of \(\pm 1.96/\sqrt{T}\) as for ordinary autocorrelations, and these are typically shown on the plot as in Figure 9.9.

`%>% ACF(Consumption) %>% autoplot() us_change `

`%>% PACF(Consumption) %>% autoplot() us_change `

If the data are from an ARIMA(\(p\),\(d\),0) or ARIMA(0,\(d\),\(q\)) model, then the ACF and PACF plots can be helpful in determining the value of \(p\) or \(q\).^{16} If \(p\) and \(q\) are both positive, then the plots do not help in finding suitable values of \(p\) and \(q\).

The data may follow an ARIMA(\(p\),\(d\),0) model if the ACF and PACF plots of the differenced data show the following patterns:

- the ACF is exponentially decaying or sinusoidal;
- there is a significant spike at lag \(p\) in the PACF, but none beyond lag \(p\).

The data may follow an ARIMA(0,\(d\),\(q\)) model if the ACF and PACF plots of the differenced data show the following patterns:

- the PACF is exponentially decaying or sinusoidal;
- there is a significant spike at lag \(q\) in the ACF, but none beyond lag \(q\).

In Figure 9.9, we see that there are three spikes in the ACF, followed by an almost significant spike at lag 4. In the PACF, there are three significant spikes, each smaller than the previous one, and then no significant spikes thereafter (apart from one just outside the bounds at lag 22). We can ignore one significant spike in each plot if it is just outside the limits, and not in the first few lags. After all, the probability of a spike being significant by chance is about one in twenty, and we are plotting 22 spikes in each plot. The pattern in the first three spikes is what we would expect from an ARIMA(3,0,0), as the PACF is decreasing as the lag increases. So in this case, the ACF and PACF lead us to think an ARIMA(3,0,0) model might be appropriate.

```
us_change %>%
fit2 <- model(ARIMA(Consumption ~ pdq(3,0,0) + PDQ(0,0,0)))
report(fit2)
#> Series: Consumption
#> Model: ARIMA(3,0,0) w/ mean
#>
#> Coefficients:
#> ar1 ar2 ar3 constant
#> 0.2027 0.1605 0.2252 0.3046
#> s.e. 0.0691 0.0697 0.0690 0.0400
#>
#> sigma^2 estimated as 0.3332: log likelihood=-170.3
#> AIC=350.6 AICc=350.9 BIC=367
```

This model is actually slightly better than the model identified by `ARIMA()`

(with an AICc value of 350.91 compared to 352.21). The `ARIMA()`

function did not find this model because it does not consider all possible models in its search. You can make it work harder by using the arguments `stepwise=FALSE`

and `approximation=FALSE`

:

```
us_change %>%
fit3 <- model(ARIMA(Consumption ~ PDQ(0,0,0),
stepwise = FALSE, approximation = FALSE))
report(fit3)
#> Series: Consumption
#> Model: ARIMA(3,0,0) w/ mean
#>
#> Coefficients:
#> ar1 ar2 ar3 constant
#> 0.2027 0.1605 0.2252 0.3046
#> s.e. 0.0691 0.0697 0.0690 0.0400
#>
#> sigma^2 estimated as 0.3332: log likelihood=-170.3
#> AIC=350.6 AICc=350.9 BIC=367
```

We also specify particular values of `pdq()`

and `PDQ()`

that ARIMA can search for. For example, to search the best non-seasonal ARIMA model with \(p\in\{1,2,3\}\), \(q\in\{0,1,2\}\) and \(d=1\), you could use `ARIMA(y ~ pdq(1:3, 1, 0:2) + PDQ(0, 0, 0))`

. We will consider seasonal ARIMA models in Section 9.9.

This time, `ARIMA()`

has found the same model that we guessed from the ACF and PACF plots. The forecasts from this ARIMA(3,0,0) model are almost identical to those shown in Figure 9.8 for the ARIMA(2,0,2) model, so we do not produce the plot here.