New Insights into Time Series Analysis II – No Correlated Observations [IMA]

Statistical parameters are used in finance, weather, industrial, science, among other vast number of different fields to draw conclusions. They are also used to identify variability patterns on photometric data in order to select non-stochastic variations, indicative of astrophysical effects. New more efficient selection methods are mandatory to analyses the huge amount of astronomical data. Our aims are to improve the current methods used to select non-stochastic variations on non-correlated data. A new approach including a modified Strateva function are used to select non-stochastic variations. Monte-Carlo simulation and public time-domain data are used to estimate its accuracy and performance. We introduce 15 modified statistical parameters covering different features of statistical distribution, like; average, dispersion, and shape parameters. Many of dispersion and shape parameters are unbound parameters, i.e. equations which do not require the calculation of the average. Unbound parameters are computed using single loop and so decreasing running time. Moreover, the majority of them have lower error than previous ones that is mainly observed for distributions having few measurements. From estimation of uncertainties together with a noise model we introduce a non-correlated variability index. It reduces the number of misselections by about 770%. We also improve the correlated indices by reducing Etot by 18% using the even-mean. Then non-correlated indices improve the selection criteria of non-stochastic variations. The even-averages provide a better estimation of mean and median and so this work also improves the correlated variability indices proposed in the first paper of this series. We consider that the first step of this project, where we set new techniques and methods that provide a huge improve on the efficiency of selection of variable stars, is now complete.

Read this paper on arXiv…

C. Lopes and N. Cross
Thu, 24 Nov 16
12/54

Comments: 11 pages, 6 figures, paper submitted to A&A