# Is there a way to tell when a time-series can no longer be predicted by the same model?

I am modeling a time series using a multiple (dynamic) linear regression model. I suspect that at some point, the model no longer accurately predicts the true series. Is there a way to find the point where the true series and the model diverge?

Ideally I'm looking for an already implemented R-package.

There is an R package called changepoint.forecast available on Github here. It implements online changepoint models which look at the forecast residuals and check for changes within them, mainly changes/drift in the expectation and variance. It can be used with any model that produces forecasts which are expected to capture the mean and second-order structure (variance/autocovariance) of the data, including black boxes.