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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

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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.

Installation

You can install the stable version from CRAN.

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

You can install the development version from Github

# install.packages("devtools")
devtools::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
dshw Double-Seasonal Holt-Winters Forecasting
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
bizdays Number of trading days in each season
autolayer.mts Automatically create a ggplot for time series objects
baggedModel Forecasting using a bagged model
fitted.fracdiff h-step in-sample forecasts for time series models.
forecast-package Forecasting Functions for Time Series and Linear Models
Arima Fit ARIMA model to univariate time series
dm.test Diebold-Mariano test for predictive accuracy
gglagplot Time series lag ggplots
ggmonthplot Create a seasonal subseries ggplot
croston Forecasts for intermittent demand using Croston's method
forecast.stl Forecasting using stl objects
forecast.bats Forecasting using BATS and TBATS models
forecast.baggedModel Forecasting using a bagged model
fourier Fourier terms for modelling seasonality
mstl Multiple seasonal decomposition
rwf Naive and Random Walk Forecasts
na.interp Interpolate missing values in a time series
plot.Arima Plot characteristic roots from ARIMA model
easter Easter holidays in each season
ndiffs Number of differences required for a stationary series
nnetar Neural Network Time Series Forecasts
msts Multi-Seasonal Time Series
tbats.components Extract components of a TBATS model
residuals.forecast Residuals for various time series models
seasadj Seasonal adjustment
forecast.mts Forecasting time series
plot.bats Plot components from BATS model
forecast.nnetar Forecasting using neural network models
thetaf Theta method forecast
CVar k-fold Cross-Validation applied to an autoregressive model
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
CV Cross-validation statistic
checkresiduals Check that residuals from a time series model look like white noise
sindexf Forecast seasonal index
tsclean Identify and replace outliers and missing values in a time series
tsCV Time series cross-validation
simulate.ets Simulation from a time series model
forecast Forecasting time series
forecast.fracdiff Forecasting using ARIMA or ARFIMA models
forecast.StructTS Forecasting using Structural Time Series models
forecast.HoltWinters Forecasting using Holt-Winters objects
plot.ets Plot components from ETS model
woolyrnq Quarterly production of woollen yarn in Australia
getResponse Get response variable from time series model.
plot.forecast Forecast plot
subset.ts Subsetting a time series
splinef Cubic Spline Forecast
gghistogram Histogram with optional normal and kernel density functions
modelAR Time Series Forecasts with a user-defined model
monthdays Number of days in each season
autoplot.mforecast Multivariate forecast plot
seasonaldummy Seasonal dummy variables
reexports Objects exported from other packages
taylor Half-hourly electricity demand
seasonal Extract components from a time series decomposition
BoxCox Box Cox Transformation
BoxCox.lambda Automatic selection of Box Cox transformation parameter
accuracy Accuracy measures for a forecast model
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
arfima Fit a fractionally differenced ARFIMA model
findfrequency Find dominant frequency of a time series
ets Exponential smoothing state space model
forecast.mlm Forecast a multiple linear model with possible time series components
gas Australian monthly gas production
is.acf Is an object a particular model type?
nsdiffs Number of differences required for a seasonally stationary series
ggseasonplot Seasonal plot
is.forecast Is an object a particular forecast type?
ocsb.test Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
StatForecast Forecast plot
forecast.modelAR Forecasting using user-defined model
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
ses Exponential smoothing forecasts
autoplot.decomposed.ts Plot time series decomposition components using ggplot
ggtsdisplay Time series display
tslm Fit a linear model with time series components
forecast.lm Forecast a linear model with possible time series components
forecast.ets Forecasting using ETS models
is.constant Is an object constant?
gold Daily morning gold prices
ma Moving-average smoothing
tsoutliers Identify and replace outliers in a time series
meanf Mean Forecast
wineind Australian total wine sales
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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 6.1.1
NeedsCompilation yes
Packaged 2019-08-22 03:02:51 UTC; hyndman
Repository CRAN
Date/Publication 2019-08-22 11:50:02 UTC

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