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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))))

enter image description here

I am quite happy with this forecast. However, in my data, sometimes extreme outliers can happen, for example:

y[100] <- -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?

enter image description here

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ARIMA on its own can't really handle outliers. You need to use tsclean or some other similar preprocessing method.

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