Periodicity Intensity Reveals Insights into Time Series Data: Three Use Cases

TL;DR

This paper introduces a method to analyze how the strength of specific periodic signals varies over time in different real-world datasets, revealing insights not accessible through traditional analysis.

Contribution

It presents a novel approach to measure and visualize the temporal variation of periodicity intensity at specific frequencies in time series data.

Findings

24h periodicity varies synchronously in herd movement

Changes in 24h periodicity can indicate wellness in sensor data

Weekly periodicity patterns evolve during a semester

Abstract

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