R in a Nutshell

Time Series Models

Time series models are a little different from other models that we’ve seen in R. With most other models, the goal is to predict a value (the response variable) from a set of other variables (the predictor variables). Usually, we explicitly assume that there is no autocorrelation: that the sequence of observations does not matter.

With time series, we assume the opposite: we assume that previous observations help predict future observations (see Figure 23-1).

Figure 23-1. Extrapolating times series

To fit an autoregressive model to a time series, use the function ar:

ar(x, aic = TRUE, order.max = NULL,
   method=c("yule-walker", "burg", "ols", "mle", "yw"),
   na.action, series, ...)

Here is a description of the arguments to ar.

Argument	Description
x	A time series.
aic	A logical value that specifies whether the Akaike information criterion is used to choose the order of the model.	TRUE
order.max	A numeric value specifying the maximum order of the model to fit.	NULL
method	A character value that specifies the method to use for fitting the model. Specify method="yw" (or method="yule-walker") for the Yule-Walker method, method="burg" for the Burg method, method="ols" for ordinary least squares, or method="mle" for maximum likelihood estimation.	c(“yule-walker”, “burg”, “ols”, “mle”, “yw”)
na.action	A function that specifies how to handle missing values.
series	A character vector of names ...