Data mining highly multiple time series of astronomical observations

Abstract

This is a case study of data mining a large data set of astronomical interest. Our first concern is the outliers apparently existing in the data set. We used a robust method to do curve fitting and identify outliers, and estimated the occurrence intensity of outliers. We find that the occurrence intensity of outliers varies considerably over time. Besides, we designed a test which led to rejection of the hypothesis that all observation series are independent of each other. Combining this fact with our estimation of the occurrence intensity of outliers we believe there are common factors transiently acting on many series of observations. Additionally, we analyse gaps in time series and summarise simple but possibly interesting characteristics of data from a methodological viewpoint of data mining.