  A time series represents a set of observations collected over time.
Typically, these observations are captured with a uniform sampling frequency
(e.g. daily). When data points are observed in uneven time intervals the time
series is referred to as irregular or intermittent. In such scenarios, the most
common solution is to reconstruct the time series to make it regular, thus
removing its intermittency. We hypothesise that, in irregular time series, the
time at which each observation is collected may be helpful to summarise the
dynamics of the data and improve forecasting performance. We study this idea by
developing a novel automatic feature engineering framework, which focuses on
extracting information from this point of view, i.e., when each instance is
collected. We study how valuable this information is by integrating it in a
time series forecasting workflow and investigate how it compares to or
complements state-of-the-art methods for regular time series forecasting. In
the end, we contribute by providing a novel framework that tackles feature
engineering for time series from an angle previously vastly ignored. We show
that our approach has the potential to further extract more information about
time series that significantly improves forecasting performance.
