forecast v8.10

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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

Travis-CI 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
auto.arima Fit best ARIMA model to univariate time series
autolayer Create a ggplot layer appropriate to a particular data type
dshw Double-Seasonal Holt-Winters Forecasting
autoplot.decomposed.ts Plot time series decomposition components using ggplot
easter Easter holidays in each season
dm.test Diebold-Mariano test for predictive accuracy
croston Forecasts for intermittent demand using Croston's method
CV Cross-validation statistic
CVar k-fold Cross-Validation applied to an autoregressive model
baggedModel Forecasting using a bagged model
autolayer.mts Automatically create a ggplot for time series objects
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
forecast.fracdiff Forecasting using ARIMA or ARFIMA models
bizdays Number of trading days in each season
forecast.ets Forecasting using ETS models
forecast.HoltWinters Forecasting using Holt-Winters objects
findfrequency Find dominant frequency of a time series
ets Exponential smoothing state space model
forecast Forecasting time series
forecast.baggedModel Forecasting using a bagged model
gglagplot Time series lag ggplots
ma Moving-average smoothing
ggmonthplot Create a seasonal subseries ggplot
forecast.StructTS Forecasting using Structural Time Series models
meanf Mean Forecast
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
forecast.bats Forecasting using BATS and TBATS models
gas Australian monthly gas production
StatForecast Forecast plot
forecast.mlm Forecast a multiple linear model with possible time series components
gghistogram Histogram with optional normal and kernel density functions
modelAR Time Series Forecasts with a user-defined model
getResponse Get response variable from time series model.
forecast.lm Forecast a linear model with possible time series components
nsdiffs Number of differences required for a seasonally stationary series
forecast.modelAR Forecasting using user-defined model
is.acf Is an object a particular model type?
ses Exponential smoothing forecasts
ndiffs Number of differences required for a stationary series
ggseasonplot Seasonal plot
is.forecast Is an object a particular forecast type?
nnetar Neural Network Time Series Forecasts
residuals.forecast Residuals for various time series models
ocsb.test Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
seasadj Seasonal adjustment
monthdays Number of days in each season
checkresiduals Check that residuals from a time series model look like white noise
forecast.stl Forecasting using stl objects
fourier Fourier terms for modelling seasonality
mstl Multiple seasonal decomposition
fitted.fracdiff h-step in-sample forecasts for time series models.
forecast.nnetar Forecasting using neural network models
is.constant Is an object constant?
gold Daily morning gold prices
forecast-package Forecasting Functions for Time Series and Linear Models
forecast.mts Forecasting time series
msts Multi-Seasonal Time Series
autoplot.mforecast Multivariate forecast plot
na.interp Interpolate missing values in a time series
taylor Half-hourly electricity demand
woolyrnq Quarterly production of woollen yarn in Australia
plot.bats Plot components from BATS model
tbats.components Extract components of a TBATS model
thetaf Theta method forecast
sindexf Forecast seasonal index
simulate.ets Simulation from a time series model
plot.Arima Plot characteristic roots from ARIMA model
reexports Objects exported from other packages
splinef Cubic Spline Forecast
ggtsdisplay Time series display
tslm Fit a linear model with time series components
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
subset.ts Subsetting a time series
wineind Australian total wine sales
tsoutliers Identify and replace outliers in a time series
plot.forecast Forecast plot
seasonal Extract components from a time series decomposition
plot.ets Plot components from ETS model
tsclean Identify and replace outliers and missing values in a time series
rwf Naive and Random Walk Forecasts
seasonaldummy Seasonal dummy variables
tsCV Time series cross-validation
BoxCox Box Cox Transformation
accuracy Accuracy measures for a forecast model
Arima Fit ARIMA model to univariate time series
BoxCox.lambda Automatic selection of Box Cox transformation parameter
arima.errors Errors from a regression model with ARIMA errors
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
arimaorder Return the order of an ARIMA or ARFIMA model
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
arfima Fit a fractionally differenced ARFIMA 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 http://pkg.robjhyndman.com/forecast, https://github.com/robjhyndman/forecast
VignetteBuilder knitr
Encoding UTF-8
RoxygenNote 7.0.1.9000
NeedsCompilation yes
Packaged 2019-12-03 22:35:15 UTC; robjhyndman
Repository CRAN
Date/Publication 2019-12-05 12:10:02 UTC

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