forecast v8.8

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

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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 2019-08-02 04:54:46 UTC; hyndman
Repository CRAN
Date/Publication 2019-08-02 15:10:02 UTC

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