forecast (8.3)

Forecasting Functions for Time Series and Linear Models.

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintainer: Rob Hyndman
Author(s): Rob Hyndman [aut, cre, cph] (<>), George Athanasopoulos [aut], Christoph Bergmeir [aut] (<>), Gabriel Caceres [aut], Leanne Chhay [aut], Mitchell O'Hara-Wild [aut], Fotios Petropoulos [aut] (<>), Slava Razbash [aut], Earo Wang [aut], Farah Yasmeen [aut] (<>), R Core Team [ctb, cph], Ross Ihaka [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb] (<>), Zhenyu Zhou [ctb]

License: GPL-3

Uses: colorspace, fracdiff, ggplot2, lmtest, magrittr, nnet, Rcpp, timeDate, tseries, urca, uroot, zoo, expsmooth, testthat, knitr, rmarkdown, rticles
Reverse depends: bfast, caschrono, ChangeAnomalyDetection, demography, dendrometeR, Dowd, EnvCpt, expsmooth, fma, forecastHybrid, forecTheta, forega, fpp, fpp2, ftsa, hts, MAPA, Mcomp, nnfor, portes, RcmdrPlugin.epack, Rlgt, Rssa, spTimer, thief, Tushare, ZRA
Reverse suggests: AER, aurelius, caschrono, corset, dplR, epimdr, gamclass, ggfortify, lifecontingencies, mFilter, origami, pander, pmml, portes, rainbow, smooth, sophisthse, tactile, trajectories, tsbox, XLConnect
Reverse enhances: tsDyn

Released over 1 year ago.