AutoLag:自动发现流数据中的滞后相关性

Yasushi Sakurai, S. Papadimitriou, C. Faloutsos
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引用次数: 8

摘要

我们介绍了流数据的自动滞后相关性检测问题,并提出了AutoLag,通过使用仔细的近似和平滑来解决这个问题。我们在真实和现实数据上的实验表明,AutoLag的工作效果与预期的一样,能够以优异的精度和显著的速度估计未知滞后。在我们对真实和现实数据的实验中,AutoLag比原始实现快约42,000倍,相对误差最多为1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AutoLag: automatic discovery of lag correlations in stream data
We have introduced the problem of automatic lag correlation detection on streaming data and proposed AutoLag to address this problem by using careful approximations and smoothing. Our experiments on real and realistic data show that AutoLag works as expected, estimating the unknown lags with excellent accuracy and significant speed-up. In our experiments on real and realistic data, AutoLag was up to about 42,000 times faster than the naive implementation, with at most 1% relative error.
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