New paper was presented by Edgar Acuña this summer:
R. Trespalacios, E. Acuña, V. Palomino, J. Agosto, R. Mégret, M. A. Giannoni-Guzman, “Applying Functional Data Clustering for Analyzing Circadian Cycles of Honeybees”, International Conference on Data Mining (ICDM), Istanbul, Turkey, July 2018.
Abstract: In this paper, we analyze the periodic cycle of honey- bees when they have between 7 and 9 days of age. The circadian clock of the bees present very erratic behavior that it is a challenge to detect cycles. In signal processing, there are several methods to detect periodic patterns. In here, we will use a well-known test, named periodogram, to evaluate rhythmicity and estimate the period. Besides, to determine whether or no rhythmicity exists, we estimate the time when the bees behavior starts to be rhythmic. Also, it can occur that the bees behavior never gets rhythmic. We perform consecutive test of rhythmicity until find out periodicity, if this exists. The instant in which the time series becomes periodic is considered the moment in which the bees activity starts. Furthermore, we carry out the periodicity test for the time series obtained from the actogram. We find out that for bees which time series is visually periodic, our method detects correctly the starting time. However, for bees which time series does not show a cyclic pattern our method fails due to a very erratic time series and that the consecutive test results also will show this erratic behavior. Finally, we classify the bees according to theirs beginning of a periodic cycle, using functional data analysis.