forecast v7.3

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by Rob Hyndman

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 Travis-CI Build Status Coverage Status CRAN_Status_Badge Downloads Rdoc Pending Pull-Requests

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 on R 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)

# ETS forecasts
fit <- ets(USAccDeaths)
plot(forecast(fit))

# Automatic ARIMA forecasts
fit <- auto.arima(WWWusage)
plot(forecast(fit, h=20))

# ARFIMA forecasts
library(fracdiff)
x <- fracdiff.sim( 100, ma=-.4, d=.3)$series
fit <- arfima(x)
plot(forecast(fit, h=30))

# Forecasting with STL
tsmod <- stlm(USAccDeaths, modelfunction=ar)
plot(forecast(tsmod, h=36))

plot(stlf(AirPassengers, lambda=0))

decomp <- stl(USAccDeaths,s.window="periodic")
plot(forecast(decomp))

# TBATS forecasts
fit <- tbats(USAccDeaths)
plot(forecast(fit))

taylor.fit <- tbats(taylor)
plot(forecast(taylor.fit))

License

This package is free and open source software, licensed under GPL (>= 2).

Functions in forecast

Name Description
autoplot.decomposed.ts ggplot of a decomposed time series object
arima.errors ARIMA errors
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation
auto.arima Fit best ARIMA model to univariate time series
accuracy Accuracy measures for forecast model
arimaorder Return the order of an ARIMA or ARFIMA model
arfima Fit a fractionally differenced ARFIMA model
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
Arima Fit ARIMA model to univariate time series
autoplot.stl ggplot STL object
CV Cross-validation statistic
dm.test Diebold-Mariano test for predictive accuracy
bizdays Number of trading days in each season
dshw Double-Seasonal Holt-Winters Forecasting
BoxCox Box Cox Transformation
autoplot.ts Automatically create a ggplot for time series objects
easter Easter holidays in each season
BoxCox.lambda Automatic selection of Box Cox transformation parameter
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
croston Forecasts for intermittent demand using Croston's method
fitted.tbats h-step in-sample forecasts using tbats models
fitted.Arima h-step in-sample forecasts using ARIMA models
ets Exponential smoothing state space model
forecast.ets Forecasting using ETS models
fitted.bats h-step in-sample forecasts using bats models
findfrequency Find dominant frequency of a time series
forecast.bats Forecasting using BATS and TBATS models
fitted.ets h-step in-sample forecasts using ets models
forecast.Arima Forecasting using ARIMA or ARFIMA models
fitted.nnetar h-step in-sample forecasts using nnetar models
forecast.StructTS Forecasting using Structural Time Series models
forecast.mlm Forecast a multiple linear model with possible time series components
forecast.HoltWinters Forecasting using Holt-Winters objects
forecast.lm Forecast a linear model with possible time series components
gas Australian monthly gas production
forecast.stl Forecasting using stl objects
forecast.nnetar Forecasting using neural network models
fortify.forecast Fortify a forecast object to data.frame for ggplot
plot.forecast Forecast plot
forecast Forecasting time series
ggmonthplot Create a seasonal subseries ggplot
gglagplot Time series lag ggplots
geom_forecast Forecast plot
is.constant Is an object constant?
gold Daily morning gold prices
is.ets Is an object a particular model type?
na.interp Interpolate missing values in a time series
naive Naive and Random Walk Forecasts
is.forecast Is an object a particular forecast type?
getResponse Get response variable from time series model.
ma Moving-average smoothing
logLik.ets Log-Likelihood of an ets object
plot.Arima Plot characteristic roots from ARIMA model
wineind Australian total wine sales
seasonaldummy Seasonal dummy variables
plot.bats Plot components from BATS model
seasadj Seasonal adjustment
woolyrnq Quarterly production of woollen yarn in Australia
sindexf Forecast seasonal index
simulate.ets Simulation from a time series model
taylor Half-hourly electricity demand
tbats.components Extract components of a TBATS model
plot.ets Plot components from ETS model
plot.mforecast Multivariate forecast plot
tsoutliers Identify and replace outliers in a time series
monthdays Number of days in each season
tslm Fit a linear model with time series components
msts Multi-Seasonal Time Series
subset.ts Subsetting a time series
splinef Cubic Spline Forecast
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
thetaf Theta method forecast
tsclean Identify and replace outliers and missing values in a time series
ndiffs Number of differences required for a stationary series
tsdisplay Time series display
nnetar Neural Network Time Series Forecasts
mforecast Forecasting time series
seasonplot Seasonal plot
meanf Mean Forecast
ses Exponential smoothing forecasts
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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 (>= 2)
URL http://github.com/robjhyndman/forecast
NeedsCompilation yes
Packaged 2016-10-12 10:12:00 UTC; robjhyndman
Repository CRAN
Date/Publication 2016-10-13 00:38:06

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