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TISEAN (acronym for Time Series Analysis) is a software package for the analysis of time series with methods based on the theory of nonlinear dynamical systems.It was developed by Rainer Hegger, Holger Kantz and Thomas Schreiber and is distributed under the GPL licence.
Programming with Big Data in R (pbdR) – a series of R packages enhanced by SPMD parallelism for big data analysis; R Commander – GUI interface for R; Rattle GUI – GUI interface for R; Revolution Analytics – production-grade software for the enterprise big data analytics; RStudio – GUI interface and development environment for R; ROOT ...
David S. Stoffer is an American statistician, and Professor Emeritus of Statistics at the University of Pittsburgh. [1] He is the author of several books on time series analysis, Time Series Analysis and Its Applications: With R Examples [2] with R.H. Shumway, Nonlinear Time Series: Theory, Methods, and Applications with R Examples [3] with R. Douc and E. Moulines, and Time Series: A Data ...
Traces is a Python library for analysis of unevenly spaced time series in their unaltered form.; CRAN Task View: Time Series Analysis is a list describing many R (programming language) packages dealing with both unevenly (or irregularly) and evenly spaced time series and many related aspects, including uncertainty.
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]
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.
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.