Domain Specific Concept Drift Detectors for Predicting Financial Time Series: the case of the SPY time series

Concept drift detectors are used in combination with learning systems to maintain a good accuracy rate on non-stationary data streams<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>. 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 and reports an experimentation on the SPY time series.