  This paper presents a practical approach for detecting non-stationarity in
time series prediction. This method is called SAFE and works by monitoring the
evolution of the spectral contents of time series through a distance function.
This method is designed to work in combination with state-of-the-art machine
learning methods in real time by informing the online predictors to perform
necessary adaptation when a non-stationarity presents. We also propose an
algorithm to proportionally include some past data in the adaption process to
overcome the Catastrophic Forgetting problem. To validate our hypothesis and
test the effectiveness of our approach, we present comprehensive experiments in
different elements of the approach involving artificial and real-world
datasets. The experiments show that the proposed method is able to
significantly save computational resources in term of processor or GPU cycles
while maintaining high prediction performances.
