Package: forecast 8.23.0

Rob Hyndman

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.

Authors:Rob Hyndman [aut, cre, cph], George Athanasopoulos [aut], Christoph Bergmeir [aut], Gabriel Caceres [aut], Leanne Chhay [aut], Kirill Kuroptev [aut], Mitchell O'Hara-Wild [aut], Fotios Petropoulos [aut], Slava Razbash [aut], Earo Wang [aut], Farah Yasmeen [aut], Federico Garza [ctb], Daniele Girolimetto [ctb], Ross Ihaka [ctb, cph], R Core Team [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb], Xiaoqian Wang [ctb], Zhenyu Zhou [ctb]

forecast_8.23.0.tar.gz
forecast_8.23.0.tar.gz(r-4.5-noble)forecast_8.23.0.tar.gz(r-4.4-noble)
forecast_8.23.0.tgz(r-4.4-emscripten)forecast_8.23.0.tgz(r-4.3-emscripten)
forecast.pdf |forecast.html
forecast/json (API)
NEWS

# Install 'forecast' in R:
install.packages('forecast', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/robjhyndman/forecast/issues

Pkgdown:https://pkg.robjhyndman.com

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • gas - Australian monthly gas production
  • gold - Daily morning gold prices
  • taylor - Half-hourly electricity demand
  • wineind - Australian total wine sales
  • woolyrnq - Quarterly production of woollen yarn in Australia

openblascpp

12.67 score 2 stars 232 packages 16k scripts 213k downloads 51 mentions 101 exports 44 dependencies

Last updated 6 months agofrom:2b9d80fa5a. Checks:OK: 1 WARNING: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-linux-x86_64WARNINGNov 29 2024

Exports:%>%accuracyAcfarfimaArimaarima.errorsarimaorderauto.arimaautolayerautoplotbaggedETSbaggedModelbatsbizdaysbld.mbb.bootstrapBoxCoxBoxCox.lambdaCcfcheckresidualscrostonCVCVardm.testdshweasteretsfindfrequencyforecastforecast.etsfourierfourierfgeom_forecastGeomForecastgetResponseggAcfggCcfgghistogramgglagchullgglagplotggmonthplotggPacfggseasonplotggsubseriesplotggtaperedacfggtaperedpacfggtsdisplayholthwInvBoxCoxis.acfis.Arimais.baggedModelis.batsis.constantis.etsis.forecastis.mforecastis.modelARis.nnetaris.nnetarmodelsis.splineforecastis.stlmmameanfmodelARmodeldfmonthdaysmstlmstsna.interpnaivendiffsnnetarnsdiffsocsb.testPacfremainderrwfseasadjseasonalseasonaldummyseasonaldummyfseasonplotsessindexfsnaivesplinefStatForecaststlfstlmtaperedacftaperedpacftbatstbats.componentsthetaftrendcycletscleantsCVtsdisplaytslmtsoutliers

Dependencies:clicolorspacecurlfansifarverfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmunsellnlmennetpillarpkgconfigquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo

Automatic Time Series Forecasting: the forecast Package for R

Rendered fromJSS2008.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2023-08-31
Started: 2017-02-23

Readme and manuals

Help Manual

Help pageTopics
forecast: Forecasting Functions for Time Series and Linear Modelsforecast-package
Accuracy measures for a forecast modelaccuracy.default
(Partial) Autocorrelation and Cross-Correlation Function EstimationAcf Ccf Pacf taperedacf taperedpacf
Fit a fractionally differenced ARFIMA modelarfima
Fit ARIMA model to univariate time seriesArima as.character.Arima print.ARIMA summary.Arima
Errors from a regression model with ARIMA errorsarima.errors
Return the order of an ARIMA or ARFIMA modelarimaorder
Fit best ARIMA model to univariate time seriesauto.arima
Create a ggplot layer appropriate to a particular data typeautolayer
Automatically create a ggplot for time series objectsautolayer.msts autolayer.mts autolayer.ts autoplot.msts autoplot.mts autoplot.ts fortify.ts
ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plottingautoplot.acf autoplot.mpacf ggAcf ggCcf ggPacf ggtaperedacf ggtaperedpacf
Plot time series decomposition components using ggplotautoplot.decomposed.ts autoplot.mstl autoplot.seas autoplot.stl autoplot.StructTS
Multivariate forecast plotautolayer.mforecast autoplot.mforecast plot.mforecast
Forecasting using a bagged modelbaggedETS baggedModel print.baggedModel
BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)as.character.bats bats print.bats
Number of trading days in each seasonbizdays
Box-Cox and Loess-based decomposition bootstrap.bld.mbb.bootstrap
Box Cox TransformationBoxCox InvBoxCox
Automatic selection of Box Cox transformation parameterBoxCox.lambda
Check that residuals from a time series model look like white noisecheckresiduals
Forecasts for intermittent demand using Croston's methodcroston
Cross-validation statisticCV
k-fold Cross-Validation applied to an autoregressive modelCVar print.CVar
Diebold-Mariano test for predictive accuracydm.test
Double-Seasonal Holt-Winters Forecastingdshw
Easter holidays in each seasoneaster
Exponential smoothing state space modelas.character.ets coef.ets ets print.ets summary.ets tsdiag.ets
Find dominant frequency of a time seriesfindfrequency
h-step in-sample forecasts for time series models.fitted.ar fitted.ARFIMA fitted.Arima fitted.bats fitted.ets fitted.forecast_ARIMA fitted.modelAR fitted.nnetar fitted.tbats
Forecasting using a bagged modelforecast.baggedModel
Forecasting using BATS and TBATS modelsforecast.bats forecast.tbats
Forecasting using ETS modelsforecast.ets
Forecasting using ARIMA or ARFIMA modelsforecast.ar forecast.Arima forecast.forecast_ARIMA forecast.fracdiff
Forecasting using Holt-Winters objectsforecast.HoltWinters
Forecast a linear model with possible time series componentsforecast.lm
Forecast a multiple linear model with possible time series componentsforecast.mlm
Forecasting using user-defined modelforecast.modelAR
Forecasting time seriesas.data.frame.mforecast forecast.mts mforecast print.mforecast summary.mforecast
Forecasting using neural network modelsforecast.nnetar
Forecasting using stl objectsforecast.stl forecast.stlm stlf stlm
Forecasting using Structural Time Series modelsforecast.StructTS
Forecasting time seriesas.data.frame.forecast as.ts.forecast forecast.default forecast.ts print.forecast summary.forecast
Fourier terms for modelling seasonalityfourier fourierf
Australian monthly gas productiongas
Get response variable from time series model.getResponse getResponse.ar getResponse.Arima getResponse.baggedModel getResponse.bats getResponse.default getResponse.fracdiff getResponse.lm getResponse.mforecast getResponse.tbats
Histogram with optional normal and kernel density functionsgghistogram
Time series lag ggplotsgglagchull gglagplot
Create a seasonal subseries ggplotggmonthplot ggsubseriesplot
Seasonal plotggseasonplot seasonplot
Time series displayggtsdisplay tsdisplay
Daily morning gold pricesgold
Is an object a particular model type?is.acf is.Arima is.baggedModel is.bats is.ets is.modelAR is.nnetar is.nnetarmodels is.stlm
Is an object constant?is.constant
Is an object a particular forecast type?is.forecast is.mforecast is.splineforecast
Moving-average smoothingma
Mean Forecastmeanf
Time Series Forecasts with a user-defined modelmodelAR print.modelAR
Compute model degrees of freedommodeldf
Number of days in each seasonmonthdays
Multiple seasonal decompositionmstl
Multi-Seasonal Time Seriesmsts print.msts window.msts `[.msts`
Interpolate missing values in a time seriesna.interp
Number of differences required for a stationary seriesndiffs
Neural Network Time Series Forecastsnnetar print.nnetar print.nnetarmodels
Number of differences required for a seasonally stationary seriesnsdiffs
Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Rootsocsb.test print.OCSBtest
Plot characteristic roots from ARIMA modelautoplot.ar autoplot.Arima plot.ar plot.Arima
Plot components from BATS modelautoplot.bats autoplot.tbats plot.bats plot.tbats
Plot components from ETS modelautoplot.ets plot.ets
Forecast plotautolayer.forecast autoplot.forecast autoplot.splineforecast plot.forecast plot.splineforecast
Residuals for various time series modelsresiduals.ar residuals.ARFIMA residuals.Arima residuals.bats residuals.ets residuals.forecast residuals.forecast_ARIMA residuals.nnetar residuals.stlm residuals.tbats residuals.tslm
Naive and Random Walk Forecastsnaive print.naive rwf snaive
Seasonal adjustmentseasadj seasadj.decomposed.ts seasadj.mstl seasadj.seas seasadj.stl seasadj.tbats
Extract components from a time series decompositionremainder seasonal trendcycle
Seasonal dummy variablesseasonaldummy seasonaldummyf
Exponential smoothing forecastsholt hw ses
Simulation from a time series modelsimulate.ar simulate.Arima simulate.ets simulate.fracdiff simulate.lagwalk simulate.modelAR simulate.nnetar simulate.tbats
Forecast seasonal indexsindexf
Cubic Spline Forecastsplinef
Forecast plotGeomForecast geom_forecast StatForecast
Subsetting a time seriessubset.msts subset.ts
Half-hourly electricity demandtaylor
TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)as.character.tbats print.tbats tbats
Extract components of a TBATS modeltbats.components
Theta method forecastthetaf
Identify and replace outliers and missing values in a time seriestsclean
Time series cross-validationtsCV
Fit a linear model with time series componentstslm
Identify and replace outliers in a time seriestsoutliers
Australian total wine saleswineind
Quarterly production of woollen yarn in Australiawoolyrnq