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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.

Readme

forecast

R build status CRAN_Status_Badge cran checks Lifecycle: retired Downloads Licence

The R package forecast provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

This package is now retired in favour of the fable package. The forecast package will remain in its current state, and maintained with bug fixes only. For the latest features and development, we recommend forecasting with the fable package.

Installation

You can install the stable version from CRAN.

install.packages('forecast', dependencies = TRUE)

You can install the development version from Github

# install.packages("remotes")
remotes::install_github("robjhyndman/forecast")

Usage

library(forecast)
library(ggplot2)

# ETS forecasts
USAccDeaths %>%
  ets() %>%
  forecast() %>%
  autoplot()

# Automatic ARIMA forecasts
WWWusage %>%
  auto.arima() %>%
  forecast(h=20) %>%
  autoplot()

# ARFIMA forecasts
library(fracdiff)
x <- fracdiff.sim( 100, ma=-.4, d=.3)$series
arfima(x) %>%
  forecast(h=30) %>%
  autoplot()

# Forecasting with STL
USAccDeaths %>%
  stlm(modelfunction=ar) %>%
  forecast(h=36) %>%
  autoplot()

AirPassengers %>%
  stlf(lambda=0) %>%
  autoplot()

USAccDeaths %>%
  stl(s.window='periodic') %>%
  forecast() %>%
  autoplot()

# TBATS forecasts
USAccDeaths %>%
  tbats() %>%
  forecast() %>%
  autoplot()

taylor %>%
  tbats() %>%
  forecast() %>%
  autoplot()

For more information

License

This package is free and open source software, licensed under GPL-3.

Functions in forecast

Name Description
Arima Fit ARIMA model to univariate time series
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
arimaorder Return the order of an ARIMA or ARFIMA model
auto.arima Fit best ARIMA model to univariate time series
arima.errors Errors from a regression model with ARIMA errors
autolayer Create a ggplot layer appropriate to a particular data type
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
autoplot.decomposed.ts Plot time series decomposition components using ggplot
croston Forecasts for intermittent demand using Croston's method
BoxCox Box Cox Transformation
BoxCox.lambda Automatic selection of Box Cox transformation parameter
forecast.fracdiff Forecasting using ARIMA or ARFIMA models
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
accuracy Accuracy measures for a forecast model
checkresiduals Check that residuals from a time series model look like white noise
easter Easter holidays in each season
bizdays Number of trading days in each season
arfima Fit a fractionally differenced ARFIMA model
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
fitted.ARFIMA h-step in-sample forecasts for time series models.
autolayer.mts Automatically create a ggplot for time series objects
dshw Double-Seasonal Holt-Winters Forecasting
forecast-package Forecasting Functions for Time Series and Linear Models
forecast.mts Forecasting time series
forecast.HoltWinters Forecasting using Holt-Winters objects
forecast.mlm Forecast a multiple linear model with possible time series components
forecast.modelAR Forecasting using user-defined model
forecast.nnetar Forecasting using neural network models
gas Australian monthly gas production
dm.test Diebold-Mariano test for predictive accuracy
StatForecast Forecast plot
forecast.baggedModel Forecasting using a bagged model
forecast.bats Forecasting using BATS and TBATS models
gglagplot Time series lag ggplots
ggtsdisplay Time series display
meanf Mean Forecast
ma Moving-average smoothing
getResponse Get response variable from time series model.
fourier Fourier terms for modelling seasonality
forecast.stl Forecasting using stl objects
gghistogram Histogram with optional normal and kernel density functions
tslm Fit a linear model with time series components
mstl Multiple seasonal decomposition
baggedModel Forecasting using a bagged model
forecast.ets Forecasting using ETS models
na.interp Interpolate missing values in a time series
plot.Arima Plot characteristic roots from ARIMA model
forecast.lm Forecast a linear model with possible time series components
plot.bats Plot components from BATS model
rwf Naive and Random Walk Forecasts
ggmonthplot Create a seasonal subseries ggplot
msts Multi-Seasonal Time Series
findfrequency Find dominant frequency of a time series
monthdays Number of days in each season
modelAR Time Series Forecasts with a user-defined model
ets Exponential smoothing state space model
is.acf Is an object a particular model type?
nnetar Neural Network Time Series Forecasts
residuals.forecast Residuals for various time series models
ndiffs Number of differences required for a stationary series
autoplot.mforecast Multivariate forecast plot
splinef Cubic Spline Forecast
is.constant Is an object constant?
forecast.StructTS Forecasting using Structural Time Series models
forecast Forecasting time series
gold Daily morning gold prices
is.forecast Is an object a particular forecast type?
plot.forecast Forecast plot
plot.ets Plot components from ETS model
seasadj Seasonal adjustment
ses Exponential smoothing forecasts
ggseasonplot Seasonal plot
reexports Objects exported from other packages
nsdiffs Number of differences required for a seasonally stationary series
ocsb.test Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
simulate.ets Simulation from a time series model
taylor Half-hourly electricity demand
sindexf Forecast seasonal index
subset.ts Subsetting a time series
thetaf Theta method forecast
tbats.components Extract components of a TBATS model
seasonal Extract components from a time series decomposition
seasonaldummy Seasonal dummy variables
tsoutliers Identify and replace outliers in a time series
wineind Australian total wine sales
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
woolyrnq Quarterly production of woollen yarn in Australia
tsclean Identify and replace outliers and missing values in a time series
tsCV Time series cross-validation
CV Cross-validation statistic
CVar k-fold Cross-Validation applied to an autoregressive model
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Vignettes of forecast

Name
JSS-paper.bib
JSS2008.Rmd
jsslogo.jpg
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Details

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
Packaged 2020-09-11 07:27:05 UTC; robjhyndman
Repository CRAN
Date/Publication 2020-09-12 06:00:08 UTC

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