Multivariate time series clustering on geophysical data recorded at 6 Mt. Etna from 1996 to 2003

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, 22and potentially useful information froma large collection of data. Finding useful similar trends inmultivariate time 23series represents a challenge in several areas including geophysics environment research. While traditional time 24series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable 25approach in the field of researchwhere different kinds of data are available. Moreover, the conventional time series 26clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data 27whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geo- 28physical multivariate time series clustering is proposed using dynamic time series segmentation and Self Orga- 29nized Maps techniques. This method allows finding coupling among trends of different geophysical data 30recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit 31eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the 32state of volcano and to define potential hazard assessment at Mt. Etna.