@Article{LopesCros:2017:IIOb,
author = "Lopes, Carlos Eduardo Ferreira and Cross, N. J. G.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Scottish
Universities Physics Alliance (SUPA)}",
title = "New insights into time series analysis: II-Non-correlated
observations",
journal = "Astronomy \& Astrophysics",
year = "2017",
volume = "604",
pages = "A121",
month = "Aug.",
keywords = "methods: data analysis – methods: statistical – techniques:
photometric – astronomical databases: miscellaneous – stars:
variables: general – infrared: general.",
abstract = "Statistical parameters are used to draw conclusions in a vast
number of fields such as finance, weather, industrial, and
science. These parameters are also used to identify variability
patterns on photometric data to select non-stochastic variations
that are indicative of astrophysical effects. New, more efficient,
selection methods are mandatory to analyze the huge amount of
astronomical data. Aims. We seek to improve the current methods
used to select non-stochastic variations on non-correlated data.
Methods. We used standard and new data-mining parameters to
analyze non-correlated data to find the best way to discriminate
between stochastic and non-stochastic variations. A new approach
that includes a modified Strateva function was performed to select
non-stochastic variations. Monte Carlo simulations and public
time-domain data were used to estimate its accuracy and
performance. Results. We introduce 16 modified statistical
parameters covering different features of statistical distribution
such as average, dispersion, and shape parameters. Many dispersion
and shape parameters are unbound parameters, i.e. equations that
do not require the calculation of average. Unbound parameters are
computed with single loop and hence decreasing running time.
Moreover, the majority of these parameters have lower errors than
previous parameters, which is mainly observed for distributions
with few measurements. A set of non-correlated variability
indices, sample size corrections, and a new noise model along with
tests of different apertures and cut-offs on the data (BAS
approach) are introduced. The number of mis-selections are reduced
by about 520% using a single waveband and 1200% combining all
wavebands. On the other hand, the even-mean also improves the
correlated indices introduced in Paper I. The mis-selection rate
is reduced by about 18% if the even-mean is used instead of the
mean to compute the correlated indices in the WFCAM database.
Even-statistics allows us to improve the effectiveness of both
correlated and non-correlated indices. Conclusions. The selection
of non-stochastic variations is improved by non-correlated
indices. The even-Averages provide a better estimation of mean and
median for almost all statistical distributions analyzed. The
correlated variability indices, which are proposed in the first
paper of this series, are also improved if the even-mean is used.
The even-parameters will also be useful for classifying light
curves in the last step of this project. We consider that the
first step of this project, where we set new techniques and
methods that provide a huge improvement on the efficiency of
selection of variable stars, is now complete. Many of these
techniques may be useful for a large number of fields. Next, we
will commence a new step of this project regarding the analysis of
period search methods.",
doi = "10.1051/0004-6361/201630109",
url = "http://dx.doi.org/10.1051/0004-6361/201630109",
issn = "0004-6361 and 1432-0746",
language = "en",
targetfile = "lopes_new.pdf",
urlaccessdate = "03 jun. 2024"
}