## 6.2 Moving averages

The classical method of time series decomposition originated in the 1920s and was widely used until the 1950s. It still forms the basis of many time series decomposition methods, so it is important to understand how it works. The first step in a classical decomposition is to use a moving average method to estimate the trend-cycle, so we begin by discussing moving averages.

### Moving average smoothing

A moving average of order $$m$$ can be written as $\begin{equation} \hat{T}_{t} = \frac{1}{m} \sum_{j=-k}^k y_{t+j}, \tag{6.1} \end{equation}$ where $$m=2k+1$$. That is, the estimate of the trend-cycle at time $$t$$ is obtained by averaging values of the time series within $$k$$ periods of $$t$$. Observations that are nearby in time are also likely to be close in value. Therefore, the average eliminates some of the randomness in the data, leaving a smooth trend-cycle component. We call this an $$m$$-MA, meaning a moving average of order $$m$$.

autoplot(elecsales) + xlab("Year") + ylab("GWh") +
ggtitle("Annual electricity sales: South Australia") Figure 6.4: Residential electricity sales (excluding hot water) for South Australia: 1989–2008.

For example, consider Figure 6.4 which shows the volume of electricity sold to residential customers in South Australia each year from 1989 to 2008 (hot water sales have been excluded). The data are also shown in Table 6.1.

Table 6.1: Annual electricity sales to residential customers in South Australia. 1989–2008.
Year Sales (GWh) 5-MA
1989 2354.34
1990 2379.71
1991 2318.52 2381.53
1992 2468.99 2424.56
1993 2386.09 2463.76
1994 2569.47 2552.60
1995 2575.72 2627.70
1996 2762.72 2750.62
1997 2844.50 2858.35
1998 3000.70 3014.70
1999 3108.10 3077.30
2000 3357.50 3144.52
2001 3075.70 3188.70
2002 3180.60 3202.32
2003 3221.60 3216.94
2004 3176.20 3307.30
2005 3430.60 3398.75
2006 3527.48 3485.43
2007 3637.89
2008 3655.00

In the last column of this table, a moving average of order 5 is shown, providing an estimate of the trend-cycle. The first value in this column is the average of the first five observations (1989–1993); the second value in the 5-MA column is the average of the values for 1990–1994; and so on. Each value in the 5-MA column is the average of the observations in the five year window centred on the corresponding year. In the notation of Equation (6.1), column 5-MA contains the values of $$\hat{T}_{t}$$ with $$k=2$$ and $$m=2k+1=5$$. This is easily computed using

ma(elecsales, 5)

There are no values for either the first two years or the last two years, because we do not have two observations on either side. Later we will use more sophisticated methods of trend-cycle estimation which do allow estimates near the endpoints.

To see what the trend-cycle estimate looks like, we plot it along with the original data in Figure 6.5.

autoplot(elecsales, series="Data") +
autolayer(ma(elecsales,5), series="5-MA") +
xlab("Year") + ylab("GWh") +
ggtitle("Annual electricity sales: South Australia") +
scale_colour_manual(values=c("Data"="grey50","5-MA"="red"),
breaks=c("Data","5-MA")) Figure 6.5: Residential electricity sales (black) along with the 5-MA estimate of the trend-cycle (red).

Notice that the trend-cycle (in red) is smoother than the original data and captures the main movement of the time series without all of the minor fluctuations. The order of the moving average determines the smoothness of the trend-cycle estimate. In general, a larger order means a smoother curve. Figure 6.6 shows the effect of changing the order of the moving average for the residential electricity sales data. Figure 6.6: Different moving averages applied to the residential electricity sales data.

Simple moving averages such as these are usually of an odd order (e.g., 3, 5, 7, etc.). This is so they are symmetric: in a moving average of order $$m=2k+1$$, the middle observation, and $$k$$ observations on either side, are averaged. But if $$m$$ was even, it would no longer be symmetric.

### Moving averages of moving averages

It is possible to apply a moving average to a moving average. One reason for doing this is to make an even-order moving average symmetric.

For example, we might take a moving average of order 4, and then apply another moving average of order 2 to the results. In the following table, this has been done for the first few years of the Australian quarterly beer production data.

beer2 <- window(ausbeer,start=1992)
ma4 <- ma(beer2, order=4, centre=FALSE)
ma2x4 <- ma(beer2, order=4, centre=TRUE)
Table 6.2: A moving average of order 4 applied to the quarterly beer data, followed by a moving average of order 2.
Year Quarter Observation 4-MA 2x4-MA
1992 Q1 443
1992 Q2 410 451.25
1992 Q3 420 448.75 450.00
1992 Q4 532 451.50 450.12
1993 Q1 433 449.00 450.25
1993 Q2 421 444.00 446.50
1993 Q3 410 448.00 446.00
1993 Q4 512 438.00 443.00
1994 Q1 449 441.25 439.62
1994 Q2 381 446.00 443.62
1994 Q3 423 440.25 443.12
1994 Q4 531 447.00 443.62
1995 Q1 426 445.25 446.12
1995 Q2 408 442.50 443.88
1995 Q3 416 438.25 440.38
1995 Q4 520 435.75 437.00
1996 Q1 409 431.25 433.50
1996 Q2 398 428.00 429.62
1996 Q3 398 433.75 430.88
1996 Q4 507 433.75 433.75

The notation “$$2\times4$$-MA” in the last column means a 4-MA followed by a 2-MA. The values in the last column are obtained by taking a moving average of order 2 of the values in the previous column. For example, the first two values in the 4-MA column are 451.25=(443+410+420+532)/4 and 448.75=(410+420+532+433)/4. The first value in the 2x4-MA column is the average of these two: 450.00=(451.25+448.75)/2.

When a 2-MA follows a moving average of an even order (such as 4), it is called a “centred moving average of order 4”. This is because the results are now symmetric. To see that this is the case, we can write the $$2\times4$$-MA as follows: \begin{align*} \hat{T}_{t} &= \frac{1}{2}\Big[ \frac{1}{4} (y_{t-2}+y_{t-1}+y_{t}+y_{t+1}) + \frac{1}{4} (y_{t-1}+y_{t}+y_{t+1}+y_{t+2})\Big] \\ &= \frac{1}{8}y_{t-2}+\frac14y_{t-1} + \frac14y_{t}+\frac14y_{t+1}+\frac18y_{t+2}. \end{align*} It is now a weighted average of observations that is symmetric. By default, the ma() function in R will return a centred moving average for even orders (unless center=FALSE is specified).

Other combinations of moving averages are also possible. For example, a $$3\times3$$-MA is often used, and consists of a moving average of order 3 followed by another moving average of order 3. In general, an even order MA should be followed by an even order MA to make it symmetric. Similarly, an odd order MA should be followed by an odd order MA.

### Estimating the trend-cycle with seasonal data

The most common use of centred moving averages is for estimating the trend-cycle from seasonal data. Consider the $$2\times4$$-MA: $\hat{T}_{t} = \frac{1}{8}y_{t-2} + \frac14y_{t-1} + \frac14y_{t} + \frac14y_{t+1} + \frac18y_{t+2}.$ When applied to quarterly data, each quarter of the year is given equal weight as the first and last terms apply to the same quarter in consecutive years. Consequently, the seasonal variation will be averaged out and the resulting values of $$\hat{T}_t$$ will have little or no seasonal variation remaining. A similar effect would be obtained using a $$2\times 8$$-MA or a $$2\times 12$$-MA to quarterly data.

In general, a $$2\times m$$-MA is equivalent to a weighted moving average of order $$m+1$$ where all observations take the weight $$1/m$$, except for the first and last terms which take weights $$1/(2m)$$. So, if the seasonal period is even and of order $$m$$, we use a $$2\times m$$-MA to estimate the trend-cycle. If the seasonal period is odd and of order $$m$$, we use a $$m$$-MA to estimate the trend-cycle. For example, a $$2\times 12$$-MA can be used to estimate the trend-cycle of monthly data and a 7-MA can be used to estimate the trend-cycle of daily data with a weekly seasonality.

Other choices for the order of the MA will usually result in trend-cycle estimates being contaminated by the seasonality in the data.

### Example: Electrical equipment manufacturing

autoplot(elecequip, series="Data") +
autolayer(ma(elecequip, 12), series="12-MA") +
xlab("Year") + ylab("New orders index") +
ggtitle("Electrical equipment manufacturing (Euro area)") +
scale_colour_manual(values=c("Data"="grey","12-MA"="red"),
breaks=c("Data","12-MA")) Figure 6.7: A 2x12-MA applied to the electrical equipment orders index.

Figure 6.7 shows a $$2\times12$$-MA applied to the electrical equipment orders index. Notice that the smooth line shows no seasonality; it is almost the same as the trend-cycle shown in Figure 6.1, which was estimated using a much more sophisticated method than a moving average. Any other choice for the order of the moving average (except for 24, 36, etc.) would have resulted in a smooth line that showed some seasonal fluctuations.

### Weighted moving averages

Combinations of moving averages result in weighted moving averages. For example, the $$2\times4$$-MA discussed above is equivalent to a weighted 5-MA with weights given by $$\left[\frac{1}{8},\frac{1}{4},\frac{1}{4},\frac{1}{4},\frac{1}{8}\right]$$. In general, a weighted $$m$$-MA can be written as $\hat{T}_t = \sum_{j=-k}^k a_j y_{t+j},$ where $$k=(m-1)/2$$, and the weights are given by $$\left[a_{-k},\dots,a_k\right]$$. It is important that the weights all sum to one and that they are symmetric so that $$a_j = a_{-j}$$. The simple $$m$$-MA is a special case where all of the weights are equal to $$1/m$$.

A major advantage of weighted moving averages is that they yield a smoother estimate of the trend-cycle. Instead of observations entering and leaving the calculation at full weight, their weights slowly increase and then slowly decrease, resulting in a smoother curve.