3.2 Some simple forecasting methods

Some forecasting methods are extremely simple and surprisingly effective. We will use the four forecasting methods as benchmarks throughout this book.

To illustrate them, we will use quarterly Australian clay brick production between 1970 and 2004.

bricks <- aus_production %>% filter_index(1970 ~ 2004)

The filter_index() function is a convenient shorthand for extracting a section of a time series.

Average method

Here, the forecasts of all future values are equal to the average (or “mean”) of the historical data. If we let the historical data be denoted by \(y_{1},\dots,y_{T}\), then we can write the forecasts as \[ \hat{y}_{T+h|T} = \bar{y} = (y_{1}+\dots+y_{T})/T. \] The notation \(\hat{y}_{T+h|T}\) is a short-hand for the estimate of \(y_{T+h}\) based on the data \(y_1,\dots,y_T\).

bricks %>% model(MEAN(Bricks))

Naïve method

For naïve forecasts, we simply set all forecasts to be the value of the last observation. That is, \[ \hat{y}_{T+h|T} = y_{T}. \] This method works remarkably well for many economic and financial time series.

bricks %>% model(NAIVE(Bricks))
# RW(Bricks) is an equivalent alternative

Because a naïve forecast is optimal when data follow a random walk (see Section 9.1), these are also called random walk forecasts.

Seasonal naïve method

A similar method is useful for highly seasonal data. In this case, we set each forecast to be equal to the last observed value from the same season of the year (e.g., the same month of the previous year). Formally, the forecast for time \(T+h\) is written as \[ \hat{y}_{T+h|T} = y_{T+h-m(k+1)}, \] where \(m=\) the seasonal period, and \(k\) is the integer part of \((h-1)/m\) (i.e., the number of complete years in the forecast period prior to time \(T+h\)). This looks more complicated than it really is. For example, with monthly data, the forecast for all future February values is equal to the last observed February value. With quarterly data, the forecast of all future Q2 values is equal to the last observed Q2 value (where Q2 means the second quarter). Similar rules apply for other months and quarters, and for other seasonal periods.

bricks %>% model(SNAIVE(Bricks ~ lag("year")))

The lag() function is optional here as bricks is monthly data and so a seasonal naïve model will need a one-year lag. However, for some time series there is more than one seasonal period, and then the required lag must be specified.

Drift method

A variation on the naïve method is to allow the forecasts to increase or decrease over time, where the amount of change over time (called the drift) is set to be the average change seen in the historical data. Thus the forecast for time \(T+h\) is given by \[ \hat{y}_{T+h|T} = y_{T} + \frac{h}{T-1}\sum_{t=2}^T (y_{t}-y_{t-1}) = y_{T} + h \left( \frac{y_{T} -y_{1}}{T-1}\right). \] This is equivalent to drawing a line between the first and last observations, and extrapolating it into the future.

bricks %>% model(RW(Bricks ~ drift()))

Example: Australian quarterly beer production

Figure 3.2 shows the first three methods applied to Australian quarterly beer production from 1992 to 2006, with the forecasts compared against actual values in the next 3.5 years.

# Set training data from 1992 to 2006
train <- aus_production %>% filter_index("1992 Q1" ~ "2006 Q4")
# Fit the models
beer_fit <- train %>%
  model(
    Mean = MEAN(Beer),
    `Naïve` = NAIVE(Beer),
    `Seasonal naïve` = SNAIVE(Beer)
  )
# Generate forecasts for 14 quarters
beer_fc <- beer_fit %>% forecast(h=14)
# Plot forecasts against actual values
beer_fc %>%
  autoplot(train, level = NULL) +
    autolayer(filter_index(aus_production, "2007 Q1" ~ .), color = "black") +
    ggtitle("Forecasts for quarterly beer production") +
    xlab("Year") + ylab("Megalitres") +
    guides(colour=guide_legend(title="Forecast"))
Forecasts of Australian quarterly beer production.

Figure 3.2: Forecasts of Australian quarterly beer production.

In this case, only the seasonal naïve forecasts are close to the observed values from 2007 onwards.

Example: Google’s daily closing stock price

In Figure 3.3, the non-seasonal methods are applied to Google’s daily closing stock price in 2015, and used to forecast one month ahead. Because stock prices are not observed every day, we first set up a new time index based on the trading days rather than calendar days.

# Re-index based on trading days
google_stock <- gafa_stock %>%
  filter(Symbol == "GOOG") %>%
  mutate(day = row_number()) %>%
  update_tsibble(index = day, regular = TRUE)
# Filter the year of interest
google_2015 <- google_stock %>% filter(year(Date) == 2015)
# Fit the models
google_fit <- google_2015 %>%
  model(
    Mean = MEAN(Close),
    `Naïve` = NAIVE(Close),
    Drift = NAIVE(Close ~ drift())
  )
# Produce forecasts for the 19 trading days in January 2015
google_fc <- google_fit %>% forecast(h = 19)
# A better way using a tsibble to determine the forecast horizons
google_jan_2016 <- google_stock %>%
  filter(yearmonth(Date) == yearmonth("2016 Jan"))
google_fc <- google_fit %>% forecast(google_jan_2016)
# Plot the forecasts
google_fc %>%
  autoplot(google_2015, level = NULL) +
    autolayer(google_jan_2016, Close, color='black') +
    ggtitle("Google stock (daily ending 31 Dec 2015)") +
    xlab("Day") + ylab("Closing Price (US$)") +
    guides(colour=guide_legend(title="Forecast"))
Forecasts based on Google's daily closing stock price in 2015.

Figure 3.3: Forecasts based on Google’s daily closing stock price in 2015.

Sometimes one of these simple methods will be the best forecasting method available; but in many cases, these methods will serve as benchmarks rather than the method of choice. That is, any forecasting methods we develop will be compared to these simple methods to ensure that the new method is better than these simple alternatives. If not, the new method is not worth considering.