Intro to Forecasting in R Part 3

Houston R Users Group Ed Goodwin, CFA

The story so far…• time series classes in R

• simple and naive forecasts

• simple linear regressions

• dummy variables

• data transformations

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

Example of Scale in Action• Monthly time series

of pigs slaughtered

• Monthly time series of pounds of milk produced

Unscaled Plot

Scaling code

Scaled plot

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)

Stationarity - Examples

Non-Stationary Stationary

Use the acf( ) functionUse acf( ) to generate an auto correlation

plot to analyze stationarity

Use the acf( ) functionUse acf( ) to generate an auto correlation

plot to analyze stationarity

Decomposing the AirPassengers Data Series

It’s easy…just use the decompose( ) function

type can be additive or multiplicative

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.

Holt-WintersUse the hw( ) function.

Holt-Winters Forecast of Air Passengers