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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 on R 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
arima.errors Errors from a regression model with ARIMA errors
arimaorder Return the order of an ARIMA or ARFIMA model
accuracy Accuracy measures for a forecast model
arfima Fit a fractionally differenced ARFIMA model
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
Arima Fit ARIMA model to univariate time series
CV Cross-validation statistic
CVar k-fold Cross-Validation applied to an autoregressive model
BoxCox Box Cox Transformation
BoxCox.lambda Automatic selection of Box Cox transformation parameter
autolayer.mts Automatically create a ggplot for time series objects
baggedETS Forecasting using the bagged ETS method
fitted.fracdiff h-step in-sample forecasts for time series models.
forecast-package Forecasting Functions for Time Series and Linear Models
forecast.baggedETS Forecasting using the bagged ETS method
forecast.bats Forecasting using BATS and TBATS models
is.constant Is an object constant?
is.acf Is an object a particular model type?
auto.arima Fit best ARIMA model to univariate time series
autolayer Create a ggplot layer appropriate to a particular data type
ets Exponential smoothing state space model
findfrequency Find dominant frequency of a time series
rwf Naive and Random Walk Forecasts
ndiffs Number of differences required for a stationary series
seasonaldummy Seasonal dummy variables
ggseasonplot Seasonal plot
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
checkresiduals Check that residuals from a time series model look like white noise
forecast.fracdiff Forecasting using ARIMA or ARFIMA models
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
autoplot.decomposed.ts Plot time series decomposition components using ggplot
dshw Double-Seasonal Holt-Winters Forecasting
easter Easter holidays in each season
forecast.mlm Forecast a multiple linear model with possible time series components
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
croston Forecasts for intermittent demand using Croston's method
dm.test Diebold-Mariano test for predictive accuracy
forecast.HoltWinters Forecasting using Holt-Winters objects
forecast.nnetar Forecasting using neural network models
forecast.stl Forecasting using stl objects
fourier Fourier terms for modelling seasonality
forecast Forecasting time series
forecast.StructTS Forecasting using Structural Time Series models
ggmonthplot Create a seasonal subseries ggplot
forecast.mts Forecasting time series
StatForecast Forecast plot
getResponse Get response variable from time series model.
nnetar Neural Network Time Series Forecasts
forecast.ets Forecasting using ETS models
forecast.lm Forecast a linear model with possible time series components
gghistogram Histogram with optional normal and kernel density functions
plot.Arima Plot characteristic roots from ARIMA model
seasadj Seasonal adjustment
seasonal Extract components from a time series decomposition
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
gas Australian monthly gas production
is.forecast Is an object a particular forecast type?
ma Moving-average smoothing
plot.forecast Forecast plot
gglagplot Time series lag ggplots
meanf Mean Forecast
monthdays Number of days in each season
plot.bats Plot components from BATS model
ses Exponential smoothing forecasts
simulate.ets Simulation from a time series model
tsclean Identify and replace outliers and missing values in a time series
gold Daily morning gold prices
msts Multi-Seasonal Time Series
na.interp Interpolate missing values in a time series
reexports Objects exported from other packages
autoplot.mforecast Multivariate forecast plot
thetaf Theta method forecast
tsCV Time series cross-validation
residuals.tslm Residuals for various time series models
subset.ts Subsetting a time series
taylor Half-hourly electricity demand
ggtsdisplay Time series display
tbats.components Extract components of a TBATS model
wineind Australian total wine sales
woolyrnq Quarterly production of woollen yarn in Australia
plot.ets Plot components from ETS model
sindexf Forecast seasonal index
splinef Cubic Spline Forecast
tslm Fit a linear model with time series components
tsoutliers Identify and replace outliers in a time series
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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://github.com/robjhyndman/forecast
VignetteBuilder knitr
RoxygenNote 6.0.1.9000
NeedsCompilation yes
Packaged 2017-06-17 04:50:32 UTC; robjhyndman
Repository CRAN
Date/Publication 2017-06-17 23:07:45 UTC

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