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Multi-Objective Optimisation in Time Series: Time Delay Agreement

机译:多目标优化及时序列:时间延迟协议

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Several time delay estimates have been reported for the quasar Q0957+561. They come from distinct data sets and published separately. This paper presents a methodology to estimate a single time delay given several data sets by using multi-objective optimisation. We use General Regression Neural Networks (GRNN) to estimate the time delay, which is one of the most accurate time delay estimators - and faster. For the time delay agreement, we use hill-climbing search. We found that the best agreement for the time delay on Q0957+561 is Δ = 420 days.
机译:据报告了几种时间延迟估计对于准Q0957 + 561。它们来自不同的数据集并单独发布。本文提出了一种方法来估计通过使用多目标优化给出了几个数据集的单个时间延迟的方法。我们使用一般回归神经网络(GRNN)来估计时间延迟,这是最准确的时间延迟估计器之一 - 并且更快。对于时间延迟协议,我们使用爬山搜索。我们发现,Q0957 + 561上的时间延迟的最佳协议是δ= 420天。

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