One of the most important concepts is most certainly Stationarity (see “unit root test"). the most common r-package will be: tseries.
see: Brockwell/Davis (2006): Time Series: Theory and Models. Brockwell/Davis (2002): Introduction to Time Series and Forecasting. Cowpertwait/Metcalfe (2009): Introductory Time Series with R. Cryer/Chan (2008): Time Series Analysis: With Applications in R. for some general introductions of using time series in r. Am 01.06.2012 um 14:49 schrieb Pierre Antoine DuBoDeNa: >> >> Hello, >> >> I am trying to collect several global measures or statistics for >> time-series as well as packages of R that can compute them. I have found >> several of them in papers and books, but the literature is so big i am sure >> i am missing several of them. >> >> skewness >> kurtosis >> min >> max >> mean >> SD >> trend >> seasonality >> periodicity >> chaos (Lyapunov Exponent) / Largest Lyapunov Exponent (i think is the same >> statistic) >> serial correlation / auto-correlation (this is the same if i am correct >> Box-Pierce autocorrelation sum) >> higher-order autocorrelation >> nonlinearity (terasvirta test) >> self similarity (Hurst exponent) >> matual information sum >> >> any other statistics that i am missing? Maybe other useful tests? >> >> or books/papers that i could find more? >> >> also any packages that can compute some/all of them? >> >> Best, >> PA >> >> >> >> > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.