24. Time Series and Autocorrelation
A big part of statistics, particularly for financial and econometric data, is analyzing time series, data that are autocorrelated over time. That is, one observation depends on previous observations and the order matters. Special care needs to be taken to account for this dependency. R has a number of built-in functions and packages to make working with time series easier.
24.1 Autoregressive Moving Average
One of the most common ways of fitting time series models is to use either autoregressive (AR), moving average (MA) or both (ARMA). These models are well represented in R and are fairly easy to work with. The formula for an ARMA(p, q) is
where
is white noise, which is essentially random data.
AR models ...
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