如何计算基于事件倍连续平滑的事件发生率？ [英] How to calculate continuous smooth event rate based on event times?

问题描述

根据连续的实时事件流（其中每一个事件的时候很容易知道，但每个事件没有任何价值，他们都是相同的），怎么程序过滤器/过程将输出一个连续平滑的事件发生率？通过连续我的意思，这例如可以绘制成的曲线，而不是仅仅倍每个事件的哪个，将得到约估计事件发生率在每一个特定的时间信息。 我知道，平稳大概可以是模糊的，但我在寻找这样做是为了使生成的曲线图的看的顺利最简单的方法。例如不应该有在每一个事件的任何大的尖峰。

Based on a continuous realtime event stream (where time of every event is easily known, but each event has no value, they are all identical), how program a filter/process that would output a continuous smooth event rate? By continuous I meant that it could for example be plotted as a graph, which instead of just times of each event, would give information about estimated event rate at each particular time. I know that "smooth" can probably be ambiguous, but I am looking for the simplest way to do this to make the resulting graph look smooth. For example there should not be any big spikes at each event.

推荐答案

通过平均。例如每次你得到一个事件添加一个到。每秒删除的1/60。它会给你用一分钟左右的滑动窗口的简单平均值。

By averaging. for example everytime you get an event add one to a. every second remove 1/60 of a. it will give you a simple average with a sliding window of about one minute.

这篇关于如何计算基于事件倍连续平滑的事件发生率？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！