  Concept drift detectors allow learning systems to maintain good accuracy on
non-stationary data streams. Financial time series are an instance of
non-stationary data streams whose concept drifts (market phases) are so
important to affect investment decisions worldwide. This paper studies how
concept drift detectors behave when applied to financial time series. General
results are: a) concept drift detectors usually improve the runtime over
continuous learning, b) their computational cost is usually a fraction of the
learning and prediction steps of even basic learners, c) it is important to
study concept drift detectors in combination with the learning systems they
will operate with, and d) concept drift detectors can be directly applied to
the time series of raw financial data and not only to the model's accuracy one.
Moreover, the study introduces three simple concept drift detectors, tailored
to financial time series, and shows that two of them can be at least as
effective as the most sophisticated ones from the state of the art when applied
to financial time series. Currently submitted to Pattern Recognition
