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.
The package will be released on CRAN when the paper is further along the publication pipeline (it was submitted earlier this year).  There is a talk describing the functionality here.
