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:许多自然发生的时间序列中都发现周期性现象,这些周期性现象是周期性变化的信号,在许多自然发生的时间序列中,经常现象是振动的信号。可以使用周期性图表来测量整个时间序列中不同频率在不同频率的振动强度,但有时我们有兴趣测量整个时间序列中特定频率的周期性强度是如何变化的。这可以通过在窗口中计算周期性强度,然后滑动并重新计算窗口的强度,从而显示整个系列中特定频率变化的周期性强度。我们介绍了这一系列中的第一个应用,第一个应用是一组新出生的小牛群的移动,其中我们展示了24小时周期性增长的强度,以及各群群群之间同步下降。我们还展示了家庭传感器所检测到的24小时活动周期性强度的变化如何能显示整体健康。我们用从23个老年人的每个家中收集的传感器数据来说明这一点。我们的第三个应用是,从虚拟学习环境(VLE)获得在线资源的数百名大学生的7天周期性强度,以及他们每周学习行为规律性如何在整个教学学期期间以可见的周期性分析方式显示其他数据的深度分析。文件显示如何显示不同时间序列。