forecast v8.4

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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
autolayer.mts Automatically create a ggplot for time series objects
forecast.mts Forecasting time series
is.constant Is an object constant?
ocsb.test Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
forecast.mlm Forecast a multiple linear model with possible time series components
Arima Fit ARIMA model to univariate time series
forecast Forecasting time series
gold Daily morning gold prices
ma Moving-average smoothing
forecast.modelAR Forecasting using user-defined model
nsdiffs Number of differences required for a seasonally stationary series
autoplot.decomposed.ts Plot time series decomposition components using ggplot
seasonal Extract components from a time series decomposition
meanf Mean Forecast
tslm Fit a linear model with time series components
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
ggtsdisplay Time series display
seasonaldummy Seasonal dummy variables
rwf Naive and Random Walk Forecasts
ets Exponential smoothing state space model
fitted.fracdiff h-step in-sample forecasts for time series models.
CV Cross-validation statistic
forecast-package Forecasting Functions for Time Series and Linear Models
is.acf Is an object a particular model type?
mstl Multiple seasonal decomposition
autolayer Create a ggplot layer appropriate to a particular data type
forecast.StructTS Forecasting using Structural Time Series models
taylor Half-hourly electricity demand
croston Forecasts for intermittent demand using Croston's method
autoplot.mforecast Multivariate forecast plot
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
fourier Fourier terms for modelling seasonality
forecast.nnetar Forecasting using neural network models
findfrequency Find dominant frequency of a time series
getResponse Get response variable from time series model.
na.interp Interpolate missing values in a time series
dm.test Diebold-Mariano test for predictive accuracy
CVar k-fold Cross-Validation applied to an autoregressive model
tsoutliers Identify and replace outliers in a time series
is.forecast Is an object a particular forecast type?
forecast.ets Forecasting using ETS models
reexports Objects exported from other packages
ses Exponential smoothing forecasts
gghistogram Histogram with optional normal and kernel density functions
ndiffs Number of differences required for a stationary series
seasadj Seasonal adjustment
nnetar Neural Network Time Series Forecasts
forecast.baggedModel Forecasting using a bagged model
baggedModel Forecasting using a bagged model
forecast.bats Forecasting using BATS and TBATS models
ggseasonplot Seasonal plot
dshw Double-Seasonal Holt-Winters Forecasting
residuals.forecast Residuals for various time series models
easter Easter holidays in each season
tsclean Identify and replace outliers and missing values in a time series
simulate.ets Simulation from a time series model
gglagplot Time series lag ggplots
plot.forecast Forecast plot
forecast.lm Forecast a linear model with possible time series components
gas Australian monthly gas production
subset.ts Subsetting a time series
plot.ets Plot components from ETS model
StatForecast Forecast plot
monthdays Number of days in each season
ggmonthplot Create a seasonal subseries ggplot
tsCV Time series cross-validation
wineind Australian total wine sales
woolyrnq Quarterly production of woollen yarn in Australia
plot.Arima Plot characteristic roots from ARIMA model
msts Multi-Seasonal Time Series
sindexf Forecast seasonal index
plot.bats Plot components from BATS model
thetaf Theta method forecast
tbats.components Extract components of a TBATS model
arima.errors Errors from a regression model with ARIMA errors
arimaorder Return the order of an ARIMA or ARFIMA model
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
checkresiduals Check that residuals from a time series model look like white noise
accuracy Accuracy measures for a forecast model
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
BoxCox Box Cox Transformation
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
forecast.HoltWinters Forecasting using Holt-Winters objects
arfima Fit a fractionally differenced ARFIMA model
auto.arima Fit best ARIMA model to univariate time series
modelAR Time Series Forecasts with a user-defined model
BoxCox.lambda Automatic selection of Box Cox transformation parameter
forecast.stl Forecasting using stl objects
splinef Cubic Spline 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 2018-06-21 04:53:37 UTC; mitchell
Repository CRAN
Date/Publication 2018-06-21 13:59:21 UTC

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