time series

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Time Series

A comparison of a variable to itself over time. One of the most common time series, especially in technical analysis, is a comparison of prices over time. For example, one may compile a time series of a security over the course of a week or a month or a year, and then use it in the determination of future price movements.

time series

A set of variables with values related to the respective times the variables are measured. Thus, a weekly record of a stock's price throughout a period of years is a time series. Time series are often used to project future values by observing how the value of a variable has changed in the past.

time series

any statistical information recorded over successive time periods. See TIME-SERIES ANALYSIS.
References in periodicals archive ?
2-9 However, this is the first study in Pakistan on time series analysis of the number of injured and fatalities due to RTCs.
2003, Time series analysis of EPN stations as a criterion of choice of reference stations for local geodynamic networks.
Chapter 1 provides a rather brief history of cyclostratigraphy, followed by an introduction to time series analysis.
Chaos time series analysis is one of the effective methods, when the cause of the complexity of the signal is the fluctuations of the nonlinear dynamical system or the internal factors.
I found many deficiencies in the book's alarmist suggestion that time series analysis is fraught with pitfalls and especially with the normative implication that it should be forsaken for other techniques.
The 1995 and 1997 papers by Pelaez contain 27 pages, most of which are devoted to presentation and discussion of the statistical results of his time series analysis of earnings, earnings growth rates and interest rate time series.
Sophisticated statistical applications, such as time series analysis and work sampling, may be used.
This book explains introductory time series analysis, presenting models and methods with examples, as well as features of realizations from various models and the use and interpretation of results based on the models.
Bayesian nonparametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc, and have been successfully applied to regression, survival analysis, language modelling, time series analysis, and visual scene analysis among others.
Among his topics are data management, graphs, summary statistics and tables, linear regression analysis, survival and event-count models, time series analysis, and multi-level and mixed-effects modeling.
Chapters on statistics with R cover statistical tests and models including probability distributions, regression models, time series analysis, bioinformatics.
Mackinlay, also published by Princeton University Press, in which the knowledge of time series analysis is assumed.