An Improved AFF Algorithm for Continuous Monitoring for Changepoints in Data Streams

Changepoints detection of online data streams is a very important issue. Adaptive estimation using a forgetting factor (briefly AFF) is an efficient algorithm for this problem. However, AFF assumes the pre-change distribution is normal, which is restrictive. In addition, AFF uses a defaulted step size 0.01. In fact, numerical results show that the step size has significant impact on the final performance of AFF algorithm, and a principle is lacking on choosing the step size. In this paper, we develop an improved AFF algorithm (briefly, IAFF). Specifically, a distribution free measure for declaring changepoints is proposed, which makes IAFF algorithm performing well for different pre-change distributions. Moreover, a general principle on choosing the step size is proposed based on intensive numerical study. Simulation results show that IAFF algorithm has much better performance than AFF in different situations.