scholarly journals How Gaps in Time-Series Data Affect Asteroseismic Interpretation

2021 ◽  
Vol 8 ◽  
Author(s):  
Paul A. Bradley
Keyword(s):  
Time Series ◽  
White Dwarf ◽  
Time Series Data ◽  
Fourier Transforms ◽  
Series Data ◽  
Frequency Content ◽  
Dwarf Stars ◽  
Advantages And Disadvantages ◽  
White Dwarf Stars ◽  
History Of

Most pulsating white dwarf stars pulsate with many periods, each of which is a probe of their interior, which has made asteroseismolgy of these stars an active field. However, disentangling the multiple periodicities requires long, uninterrupted strings of data. We briefly describe the history of multi-site observing campaigns that culminated in the development of the Whole Earth Telescope in the late 1980s that still functions today. Through examples from the May 1990 campaign on GD 358, we show how critical it is to eliminate periodic gaps in data to greatly reduce aliasing in Fourier Transforms normally used to analyze the frequency content of pulsating white dwarfs. We close with a brief description of space satellite-based data, along with the advantages and disadvantages of these data compared to ground-based data.

scholarly journals Highly comparative time-series analysis: the empirical structure of time series and their methods

2013 ◽  
Vol 10 (83) ◽  
pp. 20130048 ◽  
Author(s):  
Ben D. Fulcher ◽  
Max A. Little ◽  
Nick S. Jones
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Scientific Data ◽  
Series Data ◽  
Series Analysis ◽  
Analysis Methods ◽  
Scientific Disciplines ◽  
History Of ◽  
Reduced Representations

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

A history of university income in the United Kingdom and Australia, 1922–2017

2020 ◽  
Vol 49 (2) ◽  
pp. 229-248
Author(s):  
Tamson Pietsch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Content Type ◽  
The United Kingdom ◽  
History Of University ◽  
History Of ◽  
Strategic Influence ◽  
The Uk ◽  
Comparable Time

PurposeThe purpose of this paper is to create comparable time series data on university income in Australia and the UK that might be used as a resource for those seeking to understand the changing funding profile of universities in the two countries and for those seeking to investigate how such data were produced and utilised.Design/methodology/approachA statistical analysis of university income from all sources in the UK and Australia.FindingsThe article produces a new time series for Australia and a comparable time series for the UK. It suggests some of the ways these data related to broader patterns of economic change, sketches the possibility of strategic influence, and outlines some of their limitations.Originality/valueThis is the first study to systematically create a time series on Australian university income across the twentieth century and present it alongside a comparable dataset for the UK.

scholarly journals An Example Demonstrating the Potential for Asteroseismology of DB White Dwarf Stars

1993 ◽  
Vol 139 ◽  
pp. 116-116
Author(s):  
P.A. Bradley ◽  
M.A. Wood
Keyword(s):  
Internal Structure ◽  
Stellar Evolution ◽  
White Dwarf ◽  
White Dwarfs ◽  
Galactic Disk ◽  
Helium Surface ◽  
Dwarf Stars ◽  
University Of Texas ◽  
White Dwarf Stars ◽  
History Of

AbstractWe present the results of a parametric survey of evolutionary models of compositionally stratified white dwarfs with helium surface layers (DB white dwarfs). Because white dwarfs are the most common final end state of stellar evolution, determining their internal structure will offer us many clues about stellar evolution, the physics of matter under extreme conditions, plus the history of star formation and age of the local Galactic disk. As a first step towards determining the internal structure of DB white dwarf stars, we provide a comprehensive set of theoretical g-mode pulsation periods for comparison to observations.Because DB white dwarfs have a layered structure consisting of a helium layer overlying the carbon/oxygen core, some modes will have the same wavelength as the thickness of the helium layer, allowing a resonance to form. This resonance is called mode trapping (see Brassard et al. 1992 and references therein) and has directly observable consequences, because modes at or near the resonance have eigenfunctions and pulsation periods that are similar to each other. This results in much smaller period spacings between consecutive overtone modes of the same spherical harmonic index than the uniform period spacings seen between non-trapped modes. We demonstrate with an example how one can use the distribution of pulsation periods to determine the total stellar mass, the mass of the helium surface layer, and the extent of the helium/carbon and carbon/oxygen transition zones. With these tools, we have the prospect of being able to determine the structure of the observed DBV white dwarfs, once the requisite observations become available.We are grateful to C.J. Hansen, S.D. Kawaler, R.E. Nather, and D.E. Winget for their encouragement and many discussions. This research was supported by the National Science Foundation under grants 85-52457 and 90-14655 through the University of Texas and McDonald Observatory.

scholarly journals An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

2014 ◽  
Vol 6 (4) ◽  
pp. 2782-2808 ◽  
Author(s):  
Christopher Neigh ◽  
Douglas Bolton ◽  
Mouhamad Diabate ◽  
Jennifer Williams ◽  
Nuno Carvalhais
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Series Data ◽  
Insect Mortality ◽  
History Of

scholarly journals The Whole Earth Telescope

1989 ◽  
Vol 114 ◽  
pp. 109-114 ◽  
Author(s):  
R. Edward Nather
Keyword(s):  
Star Formation ◽  
Light Curve ◽  
Early Universe ◽  
White Dwarf ◽  
Dwarf Stars ◽  
Detailed History ◽  
White Dwarf Stars ◽  
Observational Technique ◽  
History Of ◽  
Whole Earth

AbstractThe history of our galaxy and the detailed history of star formation in the early universe is written in the white dwarf stars. Recently we have learned how to reach beneath their exposed surfaces by observing white dwarfs that are intrinsic variables. We use the stellar equivalent of seismology to probe their interiors, and thus to unravel the history they hold inside. We have designed and placed into operation an observational technique that uses the whole earth as a telescope platform, defeating the effects of daylight which, until now, had seriously limited the length of a single light curve, and therefore the amount of information we could hope to extract from it. This paper describes our new telescope and presents preliminary results from our first observing run in March, 1988.

scholarly journals Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Li ◽  
Na Zhao ◽  
Donghua Zhou ◽  
Min Cao ◽  
Jingjie Li ◽  
...  
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Power Transmission ◽  
Time Series Prediction ◽  
Series Data ◽  
Power Transmission Line ◽  
Alarm Systems ◽  
History Of ◽  
Overhead Power Transmission Line

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model’s prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.

scholarly journals An empirical survey of data augmentation for time series classification with neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254841
Author(s):  
Brian Kenji Iwana ◽  
Seiichi Uchida
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Data Augmentation ◽  
Time Series Data ◽  
Decomposition Methods ◽  
Generative Models ◽  
Series Data ◽  
Time Series Classification ◽  
Empirical Survey ◽  
Advantages And Disadvantages

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

A Single Pass Algorithm for Discovering Significant Intervals in Time-Series Data

2008 ◽  
pp. 3272-3284
Author(s):  
Sagar Savla ◽  
Sharma Chakravarthy
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Interval Data ◽  
Interval Length ◽  
Series Data ◽  
Data Set ◽  
Intrinsic Nature ◽  
Single Pass ◽  
Advantages And Disadvantages ◽  
Point Data

Sensor-based applications, such as smart homes, require prediction of event occurrences for automating the environment using time-series data collected over a period of time. In these applications, it is important to predict events in tight and accurate intervals to effectively automate the application. This article deals with the discovery of significant intervals from time-series data. Although there is a considerable body of work on sequential mining of transactional data, most of them deal with time-point data and make several passes over the entire data set in order to discover frequently occurring patterns/events. We propose an approach in which significant intervals representing intrinsic nature of data are discovered in a single pass. In our approach, time-series data is folded over a periodicity (day, week, etc.) in which the intervals are formed. Significant intervals are discovered from this interval data that satisfy the criteria of minimum confidence and maximum interval length specified by the user. Both compression and working with intervals contribute towards improving the efficiency of the algorithm. In this article, we present a new single-pass algorithm for detecting significant intervals; discuss its characteristics, advantages, and disadvantages; and analyze it. Finally, we compare the performance of our algorithm with previously developed level-wise and SQL-based algorithms for significant interval discovery (SID).

scholarly journals The Internal Structure of White Dwarf Stars Using the Whole Earth Telescope

1993 ◽  
Vol 139 ◽  
pp. 117-119
Author(s):  
P.A. Bradley
Keyword(s):  
Internal Structure ◽  
Stellar Evolution ◽  
White Dwarf ◽  
White Dwarfs ◽  
Dwarf Stars ◽  
White Dwarf Stars ◽  
History Of ◽  
The Way ◽  
Whole Earth

AbstractWhite dwarfs are the final end state for the majority of stars, and hold clues to help solve many current pressing astrophysical problems. We can perform asteroseismology on the pulsating white dwarfs to better understand their internal structure and input physics, paving the way to a better understanding of astrophysics, stellar evolution, and the history of our Galaxy. I describe briefly the potential of asteroseismology by using it to infer the internal structure of PG1159-035.

scholarly journals Identification of Cool White Dwarfs in the Sloan Digital Sky Survey

2000 ◽  
Vol 176 ◽  
pp. 514-514 ◽  
Author(s):  
T. S. Metcalfe ◽  
A. Mukadam ◽  
D. E. Winget ◽  
X. Fan ◽  
M. A. Strauss ◽  
...  
Keyword(s):  
White Dwarf ◽  
White Dwarfs ◽  
Luminosity Function ◽  
Galactic Disk ◽  
Sloan Digital Sky Survey ◽  
Dwarf Stars ◽  
White Dwarf Stars ◽  
The Galaxy ◽  
History Of ◽  
Cool White Dwarfs

AbstractWe are searching for the coolest white dwarf stars in the galactic disk and halo. The Sloan survey, in due course, will identify an enormous number of new white dwarf stars which will better define the white dwarf luminosity function—an important tool for understanding the age and history of the stellar population of the galaxy. The broadband filter data obtained in the digital photometry phase of the survey will not permit identification of the most interesting of these, the coolest white dwarf stars. This is because the cool main sequence and subdwarf stars become indistinguishable from the white dwarfs in the various colorcolor diagrams. We have interference filters designed to separate out these classes of objects. We have obtained photometry of test fields to complement the Sloan data and identify the population of cool white dwarf stars. These data will ultimately resolve the controversies, based for the most part on small-number statistics, of the location of the turndown in the white dwarf luminosity function for the disk. If the halo is significantly older than the disk, we will find a second peak in the white dwarf luminosity function, at lower luminosities than the disk turndown. Our data will provide the first meaningful constraints on the location of the turndown in the halo white dwarf luminosity function.

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