forecast: 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.

Version: 8.23.0
Depends: R (≥ 3.5.0)
Imports: colorspace, fracdiff, generics (≥ 0.1.2), ggplot2 (≥ 2.2.1), graphics, lmtest, magrittr, nnet, parallel, Rcpp (≥ 0.11.0), stats, timeDate, tseries, urca, withr, zoo
LinkingTo: Rcpp (≥ 0.11.0), RcppArmadillo (≥ 0.2.35)
Suggests: forecTheta, knitr, methods, rmarkdown, rticles, scales, seasonal, testthat (≥ 3.0.0), uroot
Published: 2024-06-20
DOI: 10.32614/CRAN.package.forecast
Author: Rob Hyndman ORCID iD [aut, cre, cph], George Athanasopoulos ORCID iD [aut], Christoph Bergmeir ORCID iD [aut], Gabriel Caceres ORCID iD [aut], Leanne Chhay [aut], Kirill Kuroptev [aut], Mitchell O'Hara-Wild ORCID iD [aut], Fotios Petropoulos ORCID iD [aut], Slava Razbash [aut], Earo Wang ORCID iD [aut], Farah Yasmeen ORCID iD [aut], Federico Garza [ctb], Daniele Girolimetto [ctb], Ross Ihaka [ctb, cph], R Core Team [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang ORCID iD [ctb], Xiaoqian Wang [ctb], Zhenyu Zhou [ctb]
Maintainer: Rob Hyndman <Rob.Hyndman at monash.edu>
BugReports: https://github.com/robjhyndman/forecast/issues
License: GPL-3
URL: https://pkg.robjhyndman.com/forecast/, https://github.com/robjhyndman/forecast
NeedsCompilation: yes
Citation: forecast citation info
Materials: README NEWS
In views: Econometrics, Environmetrics, Finance, MissingData, TimeSeries
CRAN checks: forecast results

Documentation:

Reference manual: forecast.pdf
Vignettes: Automatic Time Series Forecasting: the forecast Package for R (Hyndman & Khandakar, JSS 2008)

Downloads:

Package source: forecast_8.23.0.tar.gz
Windows binaries: r-devel: forecast_8.23.0.zip, r-release: forecast_8.23.0.zip, r-oldrel: forecast_8.23.0.zip
macOS binaries: r-release (arm64): forecast_8.23.0.tgz, r-oldrel (arm64): forecast_8.23.0.tgz, r-release (x86_64): forecast_8.23.0.tgz, r-oldrel (x86_64): forecast_8.23.0.tgz
Old sources: forecast archive

Reverse dependencies:

Reverse depends: ARIMAANN, BayesARIMAX, demography, dendrometeR, DIMORA, Dowd, ECTTDNN, EMDANNhybrid, expsmooth, fma, forecastHybrid, ForeComp, forecTheta, fpp, ftsa, garma, hts, MAPA, Mcomp, Rlgt, Rssa, StMoMo, thief, TSSVM, ZRA
Reverse imports: ACV, AEDForecasting, AFR, AriGaMyANNSVR, ATAforecasting, AutoAds, autostsm, autoTS, bayesforecast, BETS, beyondWhittle, bfast, bmgarch, caretForecast, CausalMBSTS, CEEMDANML, clmplus, coconots, ConsReg, cricketr, CvmortalityMult, daltoolbox, decomposedPSF, dendRoAnalyst, dsa, eemdARIMA, EEMDelm, eemdTDNN, epicasting, EQUALrepeat, estadistica, EventDetectR, EWSmethods, EXPAR, EXPARMA, fastcpd, fDMA, ForecastComb, forecasteR, forecastLSW, ForecastTB, fpp2, GeomComb, globaltrends, gratis, grattan, harbinger, hpiR, hybridts, iClick, imputeTestbench, imputeTS, InterNL, its.analysis, KarsTS, knnp, kssa, lfl, lineartestr, matman, memochange, midasr, mlmts, modeltime, MortalityGaps, mrf, MSGARCHelm, msltrend, netseer, ngboostForecast, nnfor, nortsTest, OBL, OOS, outliers.ts.oga, paramsim, PH1XBAR, popstudy, PortalHacienda, portalr, portes, predtoolsTS, PrInCE, PVplr, RcmdrPlugin.RiskDemo, rcrimeanalysis, SBAGM, ScottKnottESD, seastests, seer, shinyTempSignal, SLBDD, spduration, stlARIMA, stlELM, stlTDNN, stR, sweep, TCIU, Tcomp, TextForecast, timetk, TrendSLR, TSANN, tsBSS, TSclust, tsDyn, TSF, tsfeatures, tsoutliers, TSPred, tspredit, tsSelect, TSstudio, tsutils, tsviz, tswge, Tushare, uotm, utsf, VMDML, vmdTDNN, WaveletANN, WaveletArima, WaveletETS, WaveletGARCH, WaveletML, WaveletRF, WaveletSVR, WRTDStidal
Reverse suggests: AER, bayesRecon, corset, dlookr, dplR, epimdr, epimdr2, equatiomatic, fable, fableCount, flap, fracdiff, ggfortify, healthyR.ts, highcharter, ICompELM, iForecast, ivx, lazybar, lifecontingencies, MARSS, mFilter, mlr, nullabor, origami, pander, performance, pmml, PSF, rainbow, riem, sarima, setartree, tactile, trajectories, tsbox, vctsfr
Reverse enhances: texreg

Linking:

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