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In this paper we consider the problem of estimating quantiles when data arereceived sequentially (data stream). For real life data streams, thedistribution of the data typically varies with time making estimation ofquantiles challenging. We present a method that simultaneously maintainestimates of multiple quantiles of the data stream distribution. The method isbased on making incremental updates of the quantile estimates every time a newsample from the data stream is received. The method is memory andcomputationally efficient since it only stores one value for each quantileestimate and only performs one operation per quantile estimate when a newsample is received from the data stream. The estimates are realistic in thesense that the monotone property of quantiles is satisfied in every iteration.Experiments show that the method efficiently tracks multiple quantiles andoutperforms state of the art methods.

Estimation of Multiple Quantiles in Dynamically Varying Data Streams