# triple exponential - models level, trend, and seasonal components # double exponential - models level and trend Note that a series with multiplicative effects can often by transformed into series with additive effects through a log transformation (i.e., newts <- log( myts )).īoth the HoltWinters() function in the base installation, and the ets() function in the forecast package, can be used to fit exponential models.įit <- HoltWinters(myts, beta=FALSE, gamma=FALSE) Myts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))Ī time series with additive trend, seasonal, and irregular components can be decomposed using the stl() function. # subset the time series (June 2014 to December 2014) # from Jan 2009 to Dec 2014 as a time series object

# save a numeric vector containing 72 monthly observations The format is ts( vector, start=, end=, frequency=) where start and end are the times of the first and last observation and frequency is the number of observations per unit time (1=annual, 4=quartly, 12=monthly, etc.). The ts() function will convert a numeric vector into an R time series object. This section describes the creation of a time series, seasonal decomposition, modeling with exponential and ARIMA models, and forecasting with the forecast package.

R has extensive facilities for analyzing time series data.

Additive sequence of an array