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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
ets Exponential smoothing state space model
autoplot.decomposed.ts Plot time series decomposition components using ggplot
gghistogram Histogram with optional normal and kernel density functions
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
bizdays Number of trading days in each season
dm.test Diebold-Mariano test for predictive accuracy
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
Arima Fit ARIMA model to univariate time series
getResponse Get response variable from time series model.
croston Forecasts for intermittent demand using Croston's method
modelAR Time Series Forecasts with a user-defined model
monthdays Number of days in each season
autoplot.mforecast Multivariate forecast plot
reexports Objects exported from other packages
BoxCox Box Cox Transformation
BoxCox.lambda Automatic selection of Box Cox transformation parameter
findfrequency Find dominant frequency of a time series
bld.mbb.bootstrap Box-Cox and Loess-based decomposition bootstrap.
dshw Double-Seasonal Holt-Winters Forecasting
simulate.ets Simulation from a time series model
checkresiduals Check that residuals from a time series model look like white noise
sindexf Forecast seasonal index
forecast Forecasting time series
easter Easter holidays in each season
ggtsdisplay Time series display
tslm Fit a linear model with time series components
forecast.ets Forecasting using ETS models
forecast.mts Forecasting time series
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.lm Forecast a linear model with possible time series components
woolyrnq Quarterly production of woollen yarn in Australia
gas Australian monthly gas production
na.interp Interpolate missing values in a time series
rwf Naive and Random Walk Forecasts
forecast.StructTS Forecasting using Structural Time Series models
CV Cross-validation statistic
StatForecast Forecast plot
mstl Multiple seasonal decomposition
forecast.mlm Forecast a multiple linear model with possible time series components
gglagplot Time series lag ggplots
msts Multi-Seasonal Time Series
CVar k-fold Cross-Validation applied to an autoregressive model
forecast.modelAR Forecasting using user-defined model
autolayer Create a ggplot layer appropriate to a particular data type
auto.arima Fit best ARIMA model to univariate time series
ggmonthplot Create a seasonal subseries ggplot
is.acf Is an object a particular model type?
ndiffs Number of differences required for a stationary series
nnetar Neural Network Time Series Forecasts
ggseasonplot Seasonal plot
ses Exponential smoothing forecasts
forecast.stl Forecasting using stl objects
nsdiffs Number of differences required for a seasonally stationary series
tsoutliers Identify and replace outliers in a time series
fourier Fourier terms for modelling seasonality
is.forecast Is an object a particular forecast type?
plot.Arima Plot characteristic roots from ARIMA model
forecast.fracdiff Forecasting using ARIMA or ARFIMA models
ma Moving-average smoothing
meanf Mean Forecast
plot.bats Plot components from BATS model
seasonal Extract components from a time series decomposition
splinef Cubic Spline Forecast
wineind Australian total wine sales
ocsb.test Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
seasonaldummy Seasonal dummy variables
residuals.forecast Residuals for various time series models
taylor Half-hourly electricity demand
subset.ts Subsetting a time series
forecast.HoltWinters Forecasting using Holt-Winters objects
seasadj Seasonal adjustment
tsCV Time series cross-validation
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
tsclean Identify and replace outliers and missing values in a time series
forecast.baggedModel Forecasting using a bagged model
forecast.bats Forecasting using BATS and TBATS models
gold Daily morning gold prices
is.constant Is an object constant?
plot.ets Plot components from ETS model
plot.forecast Forecast plot
tbats.components Extract components of a TBATS model
thetaf Theta method forecast
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)
accuracy Accuracy measures for a forecast model
arfima Fit a fractionally differenced ARFIMA model
autolayer.mts Automatically create a ggplot for time series objects
baggedModel Forecasting using a bagged model
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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 2019-04-29 00:15:11 UTC; mitchell
Repository CRAN
Date/Publication 2019-04-29 05:40:06 UTC

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