We are interested in predicting failures of cyber-physical systems during
their operation. Particularly, we consider stochastic systems and signal
temporal logic specifications, and we want to calculate the probability that
the current system trajectory violates the specification. The paper presents
two predictive runtime verification algorithms that predict future system
states from the current observed system trajectory. As these predictions may
not be accurate, we construct prediction regions that quantify prediction
uncertainty by using conformal prediction, a statistical tool for uncertainty
quantification. Our first algorithm directly constructs a prediction region for
the satisfaction measure of the specification so that we can predict
specification violations with a desired confidence. The second algorithm
constructs prediction regions for future system states first, and uses these to
obtain a prediction region for the satisfaction measure. To the best of our
knowledge, these are the first formal guarantees for a predictive runtime
verification algorithm that applies to widely used trajectory predictors such
as RNNs and LSTMs, while being computationally simple and making no assumptions
on the underlying distribution. We present numerical experiments of an F-16
aircraft and a self-driving car