Data Mining Highly Multiple Time Series Of
Astronomical Observations
Author(s)
F. Huang
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.
Keywords: data mining, highly multiple time series, loess, MACHO project, nonparametric
curve fitting, outliers.
1 Introduction
The MACHO Project is a collaboration between scientists at the Mt. Stromlo and
Siding Spring Observatories, the Center for Particle Astrophysics at the Santa Barbara,
San Diego, and Berkeley campuses of the University of California, and the
Lawrence Livermore National Laboratory. The primary aim is to test the hypothesis
that a significant fraction of the dark matter in the halo of the Milky Way is
made up of objects like brown dwarfs or planets: these objects have come to be
known as MACHOs, for MAssive Compact Halo Objects. The signature of these
objects is the occasional amplification of the light from extragalactic stars by the
gravitational lens effect. The amplification can be large, but events are extremely
rare: it is necessary to monitor photometrically several million stars for a period of
Keywords
data mining, highly multiple time series, loess, MACHO project, nonparametric curve fitting, outliers.
Related Book
Other papers in this volume
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