forecast v8.5

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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
accuracy Accuracy measures for a forecast model
arfima Fit a fractionally differenced ARFIMA model
arimaorder Return the order of an ARIMA or ARFIMA model
BoxCox Box Cox Transformation
auto.arima Fit best ARIMA model to univariate time series
BoxCox.lambda Automatic selection of Box Cox transformation parameter
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
checkresiduals Check that residuals from a time series model look like white noise
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
forecast.fracdiff Forecasting using ARIMA or ARFIMA models
croston Forecasts for intermittent demand using Croston's method
forecast.HoltWinters Forecasting using Holt-Winters objects
ets Exponential smoothing state space model
dm.test Diebold-Mariano test for predictive accuracy
findfrequency Find dominant frequency of a time series
forecast.mlm Forecast a multiple linear model with possible time series components
forecast Forecasting time series
forecast.StructTS Forecasting using Structural Time Series models
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
forecast.baggedModel Forecasting using a bagged model
ggmonthplot Create a seasonal subseries ggplot
forecast.modelAR Forecasting using user-defined model
gglagplot Time series lag ggplots
gold Daily morning gold prices
is.constant Is an object constant?
forecast.bats Forecasting using BATS and TBATS models
getResponse Get response variable from time series model.
CV Cross-validation statistic
forecast.mts Forecasting time series
ma Moving-average smoothing
Arima Fit ARIMA model to univariate time series
gghistogram Histogram with optional normal and kernel density functions
taylor Half-hourly electricity demand
meanf Mean Forecast
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
autoplot.mforecast Multivariate forecast plot
mstl Multiple seasonal decomposition
msts Multi-Seasonal Time Series
CVar k-fold Cross-Validation applied to an autoregressive model
reexports Objects exported from other packages
woolyrnq Quarterly production of woollen yarn in Australia
splinef Cubic Spline Forecast
subset.ts Subsetting a time series
ndiffs Number of differences required for a stationary series
tbats.components Extract components of a TBATS model
forecast.nnetar Forecasting using neural network models
nnetar Neural Network Time Series Forecasts
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
thetaf Theta method forecast
bizdays Number of trading days in each season
forecast.stl Forecasting using stl objects
ggseasonplot Seasonal plot
ses Exponential smoothing forecasts
tsCV Time series cross-validation
fourier Fourier terms for modelling seasonality
fitted.fracdiff h-step in-sample forecasts for time series models.
forecast-package Forecasting Functions for Time Series and Linear Models
tsclean Identify and replace outliers and missing values in a time series
forecast.ets Forecasting using ETS models
is.acf Is an object a particular model type?
forecast.lm Forecast a linear model with possible time series components
is.forecast Is an object a particular forecast type?
plot.ets Plot components from ETS model
plot.forecast Forecast plot
gas Australian monthly gas production
simulate.ets Simulation from a time series model
StatForecast Forecast plot
modelAR Time Series Forecasts with a user-defined model
autolayer.mts Automatically create a ggplot for time series objects
baggedModel Forecasting using a bagged model
monthdays Number of days in each season
dshw Double-Seasonal Holt-Winters Forecasting
sindexf Forecast seasonal index
plot.Arima Plot characteristic roots from ARIMA model
easter Easter holidays in each season
na.interp Interpolate missing values in a time series
rwf Naive and Random Walk Forecasts
plot.bats Plot components from BATS model
residuals.forecast Residuals for various time series models
nsdiffs Number of differences required for a seasonally stationary series
tsoutliers Identify and replace outliers in a time series
seasadj Seasonal adjustment
wineind Australian total wine sales
ocsb.test Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
seasonal Extract components from a time series decomposition
seasonaldummy Seasonal dummy variables
ggtsdisplay Time series display
tslm Fit a linear model with time series components
arima.errors Errors from a regression model with ARIMA errors
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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 2019-01-16 04:32:27 UTC; hyndman
Repository CRAN
Date/Publication 2019-01-18 10:50:03 UTC

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