Event Clustering & Event Series Characterization on Expected Frequency

We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency $\Delta T^{-1}$,weintroducean$$\mathcal{O}$(N)$-efficientmethodofcharacterizing$N$eventsrepresentedbyanorderedseriesoftimestamps$t_1,t_2,\dots,t_N$. In practice, the method proves useful to e.g. identify time intervals of “missing” data or to locate “isolated events”. Moreover, we define measures to quantify a series of events by varying $\mathrm{\Delta }T$ to e.g. determine the quality of an Internet of Things service.