I have a very noisy time series like this and I forcast future values with
auto.arima from the
forecast package in R:
set.seed(123) y <- diffinv(rnorm(100)) plot(forecast(auto.arima(ts(y))))
I am quite happy with this forecast. However, in my data, sometimes extreme outliers can happen, for example:
y <- -10 plot(forecast(auto.arima(ts(y))))
These completely destroy my forecast. How can I make it so that ARIMA is robust to those outliers or would I have to detect and remove these outliers separetly?