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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
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
Arima Fit ARIMA model to univariate time series
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
dshw Double-Seasonal Holt-Winters Forecasting
bizdays Number of trading days in each season
easter Easter holidays in each season
forecast.baggedModel Forecasting using a bagged model
forecast.bats Forecasting using BATS and TBATS models
StatForecast Forecast plot
BoxCox Box Cox Transformation
BoxCox.lambda Automatic selection of Box Cox transformation parameter
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
ndiffs Number of differences required for a stationary series
getResponse Get response variable from time series model.
nnetar Neural Network Time Series Forecasts
checkresiduals Check that residuals from a time series model look like white noise
croston Forecasts for intermittent demand using Croston's method
nsdiffs Number of differences required for a seasonally stationary series
ocsb.test Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
seasonal Extract components from a time series decomposition
dm.test Diebold-Mariano test for predictive accuracy
seasonaldummy Seasonal dummy variables
forecast.mlm Forecast a multiple linear model with possible time series components
ggtsdisplay Time series display
forecast.mts Forecasting time series
is.constant Is an object constant?
is.acf Is an object a particular model type?
tslm Fit a linear model with time series components
meanf Mean Forecast
monthdays Number of days in each season
plot.forecast Forecast plot
plot.ets Plot components from ETS model
taylor Half-hourly electricity demand
woolyrnq Quarterly production of woollen yarn in Australia
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
CV Cross-validation statistic
arima.errors Errors from a regression model with ARIMA errors
arimaorder Return the order of an ARIMA or ARFIMA model
CVar k-fold Cross-Validation applied to an autoregressive model
auto.arima Fit best ARIMA model to univariate time series
autolayer Create a ggplot layer appropriate to a particular data type
autolayer.mts Automatically create a ggplot for time series objects
forecast.ets Forecasting using ETS models
forecast.fracdiff Forecasting using ARIMA or ARFIMA models
forecast.HoltWinters Forecasting using Holt-Winters objects
forecast.lm Forecast a linear model with possible time series components
ggmonthplot Create a seasonal subseries ggplot
baggedModel Forecasting using a bagged model
ets Exponential smoothing state space model
gold Daily morning gold prices
is.forecast Is an object a particular forecast type?
findfrequency Find dominant frequency of a time series
forecast Forecasting time series
ma Moving-average smoothing
gghistogram Histogram with optional normal and kernel density functions
gglagplot Time series lag ggplots
forecast.StructTS Forecasting using Structural Time Series models
plot.bats Plot components from BATS model
plot.Arima Plot characteristic roots from ARIMA model
ggseasonplot Seasonal plot
ses Exponential smoothing forecasts
tsoutliers Identify and replace outliers in a time series
mstl Multiple seasonal decomposition
wineind Australian total wine sales
msts Multi-Seasonal Time Series
residuals.forecast Residuals for various time series models
simulate.ets Simulation from a time series model
seasadj Seasonal adjustment
sindexf Forecast seasonal index
tsCV Time series cross-validation
tsclean Identify and replace outliers and missing values in a time series
accuracy Accuracy measures for a forecast model
arfima Fit a fractionally differenced ARFIMA model
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
autoplot.decomposed.ts Plot time series decomposition components using ggplot
fitted.fracdiff h-step in-sample forecasts for time series models.
forecast-package Forecasting Functions for Time Series and Linear Models
forecast.nnetar Forecasting using neural network models
forecast.stl Forecasting using stl objects
fourier Fourier terms for modelling seasonality
gas Australian monthly gas production
na.interp Interpolate missing values in a time series
autoplot.mforecast Multivariate forecast plot
rwf Naive and Random Walk Forecasts
reexports Objects exported from other packages
splinef Cubic Spline Forecast
subset.ts Subsetting a time series
tbats.components Extract components of a TBATS model
thetaf Theta method forecast
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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
RoxygenNote 6.0.1.9000
NeedsCompilation yes
Packaged 2018-04-08 01:22:14 UTC; hyndman
Repository CRAN
Date/Publication 2018-04-11 15:18:56 UTC

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