This document discusses various time series forecasting techniques in R, including scaling data, checking for stationarity, decomposing time series, and using Holt-Winters exponential smoothing. It provides examples of using the scale(), acf(), decompose(), and hw() functions in R to preprocess time series data and generate forecasts. Key transformations covered include scaling data to the same range, decomposing an air passenger time series into trend, seasonal and error components, and forecasting air passenger data using Holt-Winters exponential smoothing.

1. Intro to Forecasting in R
Part 3
Houston R Users Group
Ed Goodwin, CFA

2. The story so far…
• time series classes in R
• simple and naive forecasts
• simple linear regressions
• dummy variables
• data transformations

3. One more transformation
using scale( )
Use the scale function to scale
two different matrices. Scale
uses the formula (x-mean(x))/
sd(x) to make all data sets on
same scale.
You can use arguments to set
the whether you center on 0 or
not and whether to scale by a
certain numeric amount or
default to standard deviation

4. Example of Scale in Action
• Monthly time series
of pigs slaughtered
• Monthly time series
of pounds of milk
produced

5. Unscaled Plot

6. Scaling code

7. Scaled plot

8. Stationarity
Stationarity is a measure of how much a point in a time
series is dependent on prior points in the time series. You
must know if your data is stationary (or non-stationary) in
order to determine how to forecast.
Certain models only work on stationary time series (e.g.
ARIMA)

9. Stationarity - Examples
Non-Stationary Stationary

10. Use the acf( ) function
Use acf( ) to generate an auto correlation
plot to analyze stationarity

11. Use the acf( ) function
Use acf( ) to generate an auto correlation
plot to analyze stationarity

12. Decomposing the AirPassengers Data Series

13. It’s easy…just use the
decompose( ) function
type can be additive or multiplicative

14. Exponential Smoothing
Using Holt-Winters
Holt-Winters is a seasonal trend forecasting model. It is
a simple exponential smoothing model. This means that
it predicts the future based on a set of smoothed average
observations about the past, accounting for trend and
seasonal variations.

15. Holt-Winters
Use the hw( ) function.

16. Holt-Winters Forecast of
Air Passengers