Search results
Results from the WOW.Com Content Network
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
In statistics and econometrics, cross-sectional data is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at a single point or period of time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects, typically with no regard to differences in time.
A literature search often involves time series, cross-sectional, or panel data. Cross-panel data (CPD) is an innovative yet underappreciated source of information in the mathematical and statistical sciences. CPD stands out from other research methods because it vividly illustrates how independent and dependent variables may shift between ...
In time series analysis, Bartlett's method (also known as the method of averaged periodograms [1]), is used for estimating power spectra. It provides a way to reduce the variance of the periodogram in exchange for a reduction of resolution, compared to standard periodograms .
The R statistical software also includes many packages for time series decomposition, such as seasonal, [7] stl, stlplus, [8] and bfast. Bayesian methods are also available; one example is the BEAST method in a package Rbeast [ 9 ] in R, Matlab, and Python.
In statistics, signal processing, and econometrics, an unevenly (or unequally or irregularly) spaced time series is a sequence of observation time and value pairs (t n, X n) in which the spacing of observation times is not constant.
A time series measures the progression of one or more quantities over time. For instance, the figure above shows the level of water in the Nile river between 1870 and 1970. Change point detection is concerned with identifying whether, and if so when, the behavior of the series changes significantly. In the Nile river example, the volume of ...
In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [1] [2] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.