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This paper presents a practical approach for detecting non-stationarity intime series prediction. This method is called SAFE and works by monitoring theevolution of the spectral contents of time series through a distance function.This method is designed to work in combination with state-of-the-art machinelearning methods in real time by informing the online predictors to performnecessary adaptation when a non-stationarity presents. We also propose analgorithm to proportionally include some past data in the adaption process toovercome the Catastrophic Forgetting problem. To validate our hypothesis andtest the effectiveness of our approach, we present comprehensive experiments indifferent elements of the approach involving artificial and real-worlddatasets. The experiments show that the proposed method is able tosignificantly save computational resources in term of processor or GPU cycleswhile maintaining high prediction performances.

SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction