Mastering Data Analysis with R

Outlier detection

Besides forecasting, another time-series related major task is identifying suspicious or abnormal data in a series of observations that might distort the results of our analysis. One way to do so is to build an ARIMA model and analyze the distance between the predicted and actual values. The tsoutliers package provides a very convenient way to do so. Let's build a model on the number of cancelled flights in 2011:

> cts <- ts(daily$Cancelled)
> fit <- auto.arima(cts)
> auto.arima(cts)
Series: ts 
ARIMA(1,1,2)

Coefficients:
          ar1      ma1      ma2
      -0.2601  -0.1787  -0.7752
s.e.   0.0969   0.0746   0.0640

sigma^2 estimated as 539.8:  log likelihood=-1662.95
AIC=3333.9   AICc=3334.01   BIC=3349.49

So now we can use an ARIMA(1,1,2) model and the tso function ...