Periodic phenomena are oscillating signals found in many naturally-occurring time series. A periodogram can be used to measure the intensities of oscillations at different frequencies over an entire time series but sometimes we are interested in measuring how periodicity intensity at a specific frequency varies throughout the time series. This can be done by calculating periodicity intensity within a window then sliding and recalculating the intensity for the window, giving an indication of how periodicity intensity at a specific frequency changes throughout the series. We illustrate three applications of this the first of which is movements of a herd of new-born calves where we show how intensity of the 24h periodicity increases and decreases synchronously across the herd. We also show how changes in 24h periodicity intensity of activities detected from in-home sensors can be indicative of overall wellness. We illustrate this on several weeks of sensor data gathered from each of the homes of 23 older adults. Our third application is the intensity of 7-day periodicity of hundreds of University students accessing online resources from a virtual learning environment (VLE) and how the regularity of their weekly learning behaviours changes throughout a teaching semester. The paper demonstrates how periodicity intensity reveals insights into time series data not visible using other forms of analysis

翻译：周期性现象是许多自然出现的时间序列中常见的振荡信号。周期图可用于测量整个时间序列中不同频率的振荡强度，但有时我们感兴趣的是特定频率的周期性强度在整个时间序列中如何变化。这可以通过在窗口内计算周期性强度，然后滑动窗口并重新计算强度来实现，从而揭示特定频率的周期性强度在整个序列中的变化趋势。我们展示了这一方法的三个应用：第一个应用是关于一群新生犊牛的运动，我们展示了24小时周期性强度如何在牛群中同步增减。我们还展示了从家中传感器检测到的活动在24小时周期性强度上的变化如何反映整体健康状况。我们利用从23位老年人家庭中收集的几周传感器数据进行了说明。第三个应用则是数百名大学生从虚拟学习环境中访问在线资源的7天周期性强度，以及他们每周学习行为的规律性如何在整个教学学期中变化。本文证明了周期性强度能够揭示时间序列数据中其他分析方法无法直接观察到的洞察。