Smoothing is usually done to help us better see patterns, trends for example, in time series. Generally smooth out the irregular roughness to see a clearer signal. For seasonal data, we might smooth out the seasonality so that we can identify the trend. Smoothing doesn’t provide us with a model, but it can be a good first step in describing various components of the series.

The term **filter** is sometimes used to describe a smoothing procedure. For instance, if the smoothed value for a particular time is calculated as a linear combination of observations for surrounding times, it might be said that we’ve applied a linear filter to the data (not the same as saying the result is a straight line, by the way).

#### Moving Averages

The traditional use of the term moving average is that at each point in time we determine (possibly weighted) averages of observed values that surround a particular time.

For instance, at time \(t\), a "centered moving average of length 3" with equal weights would be the average of values at times \(t-1, t\), and \(t+1\).

To take away seasonality from a series so we can better see trend, we would use a moving average with a length = seasonal span. Thus in the smoothed series, each smoothed value has been averaged across all seasons. This might be done by looking at a “one-sided” moving average in which you average all values for the previous year’s worth of data or a centered moving average in which you use values both before and after the current time.

For quarterly data, for example, we could define a smoothed value for time \(t\) as \(\left( x _ { t } + x _ { t - 1 } + x _ { t - 2 } + x _ { t - 3 } \right) / 4\), the average of this time and the previous 3 quarters. In R code this will be a one-sided filter.

A centered moving average creates a bit of a difficulty when we have an even number of time periods in the seasonal span (as we usually do).

To smooth away seasonality in **quarterly data**, in order to identify trend, the usual convention is to use the moving average smoothed at time* *\(t\)* *is

\( \dfrac{1}{8}x_{t-2}+\dfrac{1}{4}x_{t-1}+\dfrac{1}{4}x_t +\dfrac{1}{4}x_{t+1}+\dfrac{1}{8}x_{t+2}\)

To smooth away seasonality in **monthly data**, in order to identify trend, the usual convention is to use the moving average smoothed at time* *\(t\)* *is

\( \dfrac{1}{24}x_{t-6}+\dfrac{1}{12}x_{t-5}+\dfrac{1}{12}x_{t-4} +\dots + \dfrac{1}{12}x_{t+4}+\frac{1}{12}x_{t+5}+\dfrac{1}{24}x_{t+6}\)

That is, we apply weight 1/24 to values at times \(t-6\)* *and \(t+6\) and weight 1/12 to all values at all times between \(t-5\) and \(t+5\).

In the R `filter` command, we’ll specify a two-sided filter when we want to use values that come both before and after the time for which we’re smoothing.

**Note!**

On page 71 of our book, the authors apply equal weights across a centered seasonal moving average. That’s okay too. For instance, a quarterly smoother might be smoothed at time* t *is

\( \dfrac{1}{5}x_{t-2}+\dfrac{1}{5}x_{t-1}+\dfrac{1}{5}x_t +\dfrac{1}{5}x_{t+1}+\dfrac{1}{5}x_{t+2}\)

A monthly smoother might apply a weight of 1/13 to all values from times \(t-6\) to \(t+6\).

The code the authors use on page 72 takes advantage of a `rep` command that repeats a value a certain number of times. They don’t use the “filter” parameter within the filter command.

##
Example 5-3: Quarterly Beer Production in Australia
Section* *

In both Lesson 1 and Lesson 4, we looked at a series of quarterly beer production in Australia. The following R code creates a smoothed series that lets us see the trend pattern, and plots this trend pattern on the same graph as the time series. The second command creates and stores the smoothed series in the object called trendpattern.

**Note!**

Within the filter command, the parameter named filter gives the coefficients for our smoothing and sides = 2 causes a centered smooth to be calculated.

```
beerprod = scan("beerprod.dat")
trendpattern = filter (beerprod, filter = c(1/8, 1/4, 1/4, 1/4, 1/8), sides=2)
plot (beerprod, type= "b", main = "moving average annual trend")
lines (trendpattern)
```

Here’s the result:

We might subtract the trend pattern from the data values to get a better look at seasonality. Here’s how that would be done:

```
seasonals = beerprod - trendpattern
plot (seasonals, type = "b", main = "Seasonal pattern for beer production")
```

The result follows:

Another possibility for smoothing series to see trend is the one-sided filter

`trendpattern2 = filter (beerprod, filter = c(1/4, 1/4, 1/4, 1/4), sides=1)`

With this, the smoothed value is the average of the past year.

##
Example 5-4: U.S. Monthly Unemployment
Section* *

In the homework for week 4 you looked at a monthly series of U.S. Unemployment for 1948-1978. Here’s a smoothing done to look at the trend.

```
trendunemploy = filter(unemploy, filter = c(1/24,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/24), sides = 2)
trendunemploy = ts(trendunemploy, start = c(1948,1), freq = 12)
plot (trendunemploy, main="Trend in U.S. Unemployment, 1948-1978", xlab = "Year")
```

Only the smoothed trend is plotted. The second command identifies the calendar time characteristics of the series. That makes the plot have a more meaningful axis. The plot follows.

##### Non-Seasonal Series

For non-seasonal series, you aren’t bound to smooth over any particular span. For smoothing you should experiment with moving averages of different spans. Those spans of time could be relatively short. The objective is to knock off the rough edges to see what trend or pattern might be there.

##### Other Smoothing Methods (Section 2.3)

Section 2.3 describes several sophisticated and useful alternatives to moving average smoothing. The details may seem sketchy, but that's okay because we don’t want to get bogged down in lots of details for those methods. Of the alternative methods described in Section 2.3, lowess (locally weighted regression) may be the most widely used.

#### Example Continued

The following plot is the smoothed trend line for the U.S. Unemployment series, found using a lowess smoother in which a substantial amount (2/3) contributed to each smoothed estimate.

**Note!**

This smoothed the series more aggressively than the moving average.

The commands used were

```
unemploy = ts(unemploy, start = c(1948,1), freq=12)
plot(lowess(unemploy, f = 2/3), main ="Lowess smoothing of U.S. Unemployment Trend")
```

#### Single Exponential Smoothing

The basic forecasting equation for single exponential smoothing is often given as

\( \widehat{x}_{t+1} = \alpha x_t + (1-\alpha)\widehat{x}_t \text{ (1)}\)

We forecast the value of x at time \(t\)*+1* to be a weighted combination of the observed value at time \(t\)* *and the forecasted value at time \(t\). Although the method is called a smoothing method, it’s principally used for short run forecasting.

The value of \(\alpha\) is called the **smoothing constant**. For whatever reason, \(\alpha\) = 0.2 is a popular default choice of programs. This puts a weight of .2 on the most recent observation and a weight of 1 − .2 = .8 on the most recent forecast. With a relatively small value of \(\alpha\), the smoothing will be relatively more extensive. With a relatively large value of \(\alpha\), the smoothing is relatively less extensive as more weight will be put on the observed value.

This is simple one-step ahead forecasting method that at first glance seems not to require a model for the data. In fact, this method is equivalent to the use of an ARIMA(0,1,1) model with no constant.

The optimal procedure is to fit an ARIMA (0,1,1) model to the observed dataset and use the results to determine the value of \(\alpha\). This is “optimal” in the sense of creating the best \(\alpha\) for the data already observed.

Although the goal is smoothing and one step ahead forecasting, the equivalence to the ARIMA(0,1,1) model does bring up a good point. We shouldn't blindly apply exponential smoothing because the underlying process might not be well modeled by an ARIMA(0,1,1).

#### ARIMA(0,1,1) and Exponential Smoothing Equivalence

Consider an ARIMA(0,1,1) with mean \(\mu\) = 0 for the first differences, *x _{t} - x_{t-1}* :

The model is \(x_t-x_{t-1}=w_t + \theta_1 w_{t-1}\).

Equivalently, \(x_t=x_{t-1}+w_t + \theta_1 w_{t-1}\)*.*

To forecast at time \(t+1\), we consider \(x_{t+1}=x_t+w_{t+1} +\theta_1 w_t\).

Because \(w_{t+1} = x_{t+1}-\widehat{x}_{t+1},\)

\begin{align} \widehat{x}_{t+1} & = x_t + \theta_1 w_t \\ & = x_t + \theta_1(x_t-\widehat{x}_t)\\ & = (1 + \theta_1)x_t - \theta_1\widehat{x}_t\end{align}.

If we let \(\alpha\) = (1+ \(\theta_1\)) and thus -(\(\theta_1\)) = 1−\(\alpha\), we see the equivalence to equation (1) above.

#### Why the Method is Called Exponential Smoothing

Starting with \(\widehat{x}_{t+1} = \alpha x_{t} + (1-\alpha)\widehat{x}_t\), we can substitute for \(\widehat{x}_t\).

This yields the following:

\begin{align} \widehat{x}_{t+1} & = \alpha x_t + (1-\alpha)[\alpha x_{t-1}+(1-\alpha)\widehat{x}_{t-1}]\\ & = \alpha x_t + \alpha(1-\alpha)x_{t-1} + (1-\alpha)^2\widehat{x}_{t-1}\end{align}

Continue in this fashion by successively substituting for the forecasted value on the right side of the equation. This leads to:

\begin{align} &\widehat{x}_{t+1} = \alpha x_t + \alpha(1-\alpha)x_{t-1} + \alpha(1-\alpha)^2 x_{t-2} + \dots\\ &+ \alpha(1-\alpha)^j x_{t-j} + \dots + \alpha(1-\alpha)^{t-1}x_1 \text{(2)}\end{align}

Equation 2 shows that the *forecasted value is a weighted average* of all past values of the series, with *exponentially changing weights* as we move back in the series.

#### Optimal Exponential Smoothing in R

Basically, we just fit an ARIMA(0,1,1) to the data and determine the \(\alpha\) coefficient. We can examine the fit of the smooth by comparing the predicted values to the actual series. Exponential smoothing tends to be used more as a forecasting tool than a true smoother, so we’re looking to see if we have a good fit.

##
Example 5-5
Section* *

*n* = 100 monthly observations of the logarithm of an oil price index in the United States. The data series is:

An ARIMA(0,1,1) fit in R gave an MA(1) coefficient = 0.3877. Thus \(\alpha\) = (1+ \(\theta_1\)) = 1.3877 and 1- \(\alpha\) = -0.3877. The exponential smoothing forecasting equation is

\(\widehat{x}_{t+1} = 1.3877x_t - 0.3877\widehat{x}_t\)

At time 100, the observed value of the series is *x*_{100} = 0.86601. The predicted value for the series at that time is

\(\widehat{x}_{100}= 0.856789\)

Thus the forecast for time 101 is

\(\widehat{x}_{101} = 1.3877x_{100} - 0.3877\widehat{x}_{100} = 1.3877(0.86601)-0.3877(0.856789) = 0.8696\)

Following is how well the smoother fits the series. It’s a good fit. That’s a good sign for forecasting, the main purpose for this “smoother.”

Here are the commands used to generate the output for this example:

```
oilindex = ts(scan("oildata.dat"))
plot (oilindex, type = "b", main = "Log of Oil Index Series")
expsmoothfit = arima (oilindex, order = c(0,1,1))
expsmoothfit # to see the arima results
predicteds = oilindex - expsmoothfit$residuals # predicted values
plot (oilindex, type="b", main = "Exponential Smoothing of Log of Oil Index")
lines (predicteds)
1.3877*oilindex[100]-0.3877*predicteds[100] # forecast for time 101
```

##
Double Exponential Smoothing
Section* *

Double exponential smoothing might be used when there's trend (either long run or short run), but no seasonality.

Essentially the method creates a forecast by combining exponentially smoothed estimates of the trend (slope of a straight line) and the level (basically, the intercept of a straight line).

Two different weights, or smoothing parameters, are used to update these two components at each time.

The smoothed “level” is more or less equivalent to a simple exponential smoothing of the data values and the smoothed trend is more or less equivalent to a simple exponential smoothing of the first differences.

The procedure is equivalent to fitting an ARIMA(0,2,2) model, with no constant; it can be carried out with an ARIMA(0,2,2) fit.

\[(1-B)^2 x_t = (1+\theta_1B + \theta_2B^2)w_t.\]