An adaptive estimation method with exploration and exploitation modes for non-stationary environments

Dynamic systems are highly complex and hard to deal with due to their subject-and time-varying na-ture. The fact that most of the real world systems/events are of dynamic character makes modeling and analysis of such systems inevitable and charmingly useful. One promising estimation method that is ca-pable of unlearning past information to deal with non-stationarity is Stochastic Learning Weak Estimator (SLWE) by Oommen and Rueda (2006). However, due to using a constant learning rate, it faces a trade-off between plasticity and stability. In this paper, we model SLWE as a random walk and provide rigorous theoretical analysis of asymptotic behavior of estimates to obtain a statistical model. Utilizing this model, we detect changes in stationarity to switch between exploratory and exploitative learning modes. Exper-imental evaluations on both synthetic and real world data show that the proposed method outperforms related algorithms in different types of drifts. (c) 2022 Elsevier Ltd. All rights reserved.