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- W2897770533 abstract "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." @default.
- W2897770533 created "2018-10-26" @default.
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- W2897770533 date "2018-06-23" @default.
- W2897770533 modified "2023-09-26" @default.
- W2897770533 title "An Improved AFF Algorithm for Continuous Monitoring for Changepoints in Data Streams" @default.
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- W2897770533 doi "https://doi.org/10.1145/3232829.3232842" @default.
- W2897770533 hasPublicationYear "2018" @default.
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