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X-13ARIMA-SEATS, successor to X-12-ARIMA and X-11, is a set of statistical methods for seasonal adjustment and other descriptive analysis of time series data that are implemented in the U.S. Census Bureau's software package. [3]
In time series analysis, the Box–Jenkins method, [1] named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series.
RATS is a powerful program, which can perform a range of econometric and statistical operations. The following is a list of the major procedures in econometrics and time series analysis that can be implemented in RATS. All these methods can be used in order to forecast, as well as to conduct data analysis.
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.
Microfit – econometrics package, time series; Minitab – general statistics package; MLwiN – multilevel models (free to UK academics) Nacsport Video Analysis Software – software for analysing sports and obtaining statistical intelligence; NAG Numerical Library – comprehensive math and statistics library; NCSS – general statistics package
5 Time series analysis. 6 Charts and diagrams. 7 Other abilities. 8 See also. 9 Footnotes. 10 References. 11 Further reading. Toggle the table of contents. Comparison ...
EViews is a statistical package for Windows, used mainly for time-series oriented econometric analysis. It is developed by Quantitative Micro Software (QMS), now a part of IHS. Version 1.0 was released in March 1994, and replaced MicroTSP. [1] The TSP software and programming language had been originally developed by Robert Hall in 1965. The ...
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.