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Forecast: Forecasting Functions for Time Series and Linear Models Package ‘forecast’ June 1, 2021 Version 8.15 Title Forecasting Functions for Time Series and Linear Models Description Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. Depends R (>= 3.0.2), Imports colorspace, fracdiff, ggplot2 (>= 2.2.1), graphics, lmtest, magrittr, nnet, parallel, Rcpp (>= 0.11.0), stats, timeDate, tseries, urca, zoo Suggests forecTheta, knitr, methods, rmarkdown, rticles, seasonal, testthat, uroot LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35) LazyData yes ByteCompile TRUE BugReports https://github.com/robjhyndman/forecast/issues License GPL-3 URL https://pkg.robjhyndman.com/forecast/, https://github.com/robjhyndman/forecast VignetteBuilder knitr Encoding UTF-8 RoxygenNote 7.1.1 NeedsCompilation yes Author Rob Hyndman [aut, cre, cph] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [aut], Christoph Bergmeir [aut] (<https://orcid.org/0000-0002-3665-9021>), Gabriel Caceres [aut], Leanne Chhay [aut], Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Fotios Petropoulos [aut] (<https://orcid.org/0000-0003-3039-4955>), Slava Razbash [aut], 1 2 R topics documented: Earo Wang [aut], Farah Yasmeen [aut] (<https://orcid.org/0000-0002-1479-5401>), R Core Team [ctb, cph], Ross Ihaka [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb] (<https://orcid.org/0000-0001-5243-233X>), Zhenyu Zhou [ctb] Maintainer Rob Hyndman <[email protected]> Repository CRAN Date/Publication 2021-06-01 11:20:12 UTC R topics documented: forecast-package . .4 accuracy . .4 Acf .............................................6 arfima . .9 Arima . 10 arima.errors . 13 arimaorder . 14 auto.arima . 14 autolayer . 18 autolayer.mts . 18 autoplot.acf . 20 autoplot.decomposed.ts . 22 autoplot.mforecast . 24 baggedModel . 25 bats ............................................. 27 bizdays . 29 bld.mbb.bootstrap . 30 BoxCox . 31 BoxCox.lambda . 32 checkresiduals . 33 croston . 34 CV.............................................. 36 CVar............................................. 37 dm.test . 38 dshw............................................. 40 easter . 42 ets.............................................. 43 findfrequency . 45 fitted.ARFIMA . 46 forecast . 48 forecast.baggedModel . 50 forecast.bats . 52 forecast.ets . 53 R topics documented: 3 forecast.fracdiff . 55 forecast.HoltWinters . 58 forecast.lm . 59 forecast.mlm . 61 forecast.modelAR . 63 forecast.mts . 65 forecast.nnetar . 67 forecast.stl . 70 forecast.StructTS . 73 fourier . 75 gas.............................................. 76 getResponse . 77 gghistogram . 78 gglagplot . 79 ggmonthplot . 81 ggseasonplot . 82 ggtsdisplay . 83 gold............................................. 85 is.acf . 86 is.constant . 86 is.forecast . 87 ma.............................................. 87 meanf . 88 modelAR . 90 monthdays . 92 mstl ............................................. 93 msts............................................. 94 na.interp . 95 ndiffs . 96 nnetar . 97 nsdiffs . 99 ocsb.test . 101 plot.Arima . 102 plot.bats . 104 plot.ets . 105 plot.forecast . 106 residuals.forecast . 109 rwf.............................................. 110 seasadj . 113 seasonal . 114 seasonaldummy . 115 ses.............................................. 116 simulate.ets . 119 sindexf . 122 splinef . 123 StatForecast . 125 subset.ts . 127 taylor . 129 4 accuracy tbats . 129 tbats.components . 131 thetaf . 132 tsclean . 134 tsCV............................................. 135 tslm ............................................. 136 tsoutliers . 138 wineind . 139 woolyrnq . 139 Index 140 forecast-package Forecasting Functions for Time Series and Linear Models Description Methods and tools for displaying and analysing univariate time series forecasts including exponen- tial smoothing via state space models and automatic ARIMA modelling. Details Package: forecast Type: Package License: GPL3 LazyLoad: yes Author(s) Rob J Hyndman Maintainer: [email protected] accuracy Accuracy measures for a forecast model Description Returns range of summary measures of the forecast accuracy. If x is provided, the function measures test set forecast accuracy based on x-f. If x is not provided, the function only produces training set accuracy measures of the forecasts based on f["x"]-fitted(f). All measures are defined and discussed in Hyndman and Koehler (2006). accuracy 5 Usage accuracy(object, ...) ## Default S3 method: accuracy(object, x, test =.
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