forecast v7.1

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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

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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 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
arfima Fit a fractionally differenced ARFIMA model
CV Cross-validation statistic
gold Daily morning gold prices
nnetar Neural Network Time Series Forecasts
forecast Forecasting time series
fitted.Arima One-step in-sample forecasts using ARIMA models
tslm Fit a linear model with time series components
dshw Double-Seasonal Holt-Winters Forecasting
splinef Cubic Spline Forecast
tbats.components Extract components of a TBATS model
subset.ts Subsetting a time series
logLik.ets Log-Likelihood of an ets object
arima.errors ARIMA errors
msts Multi-Seasonal Time Series
Arima Fit ARIMA model to univariate time series
woolyrnq Quarterly production of woollen yarn in Australia
thetaf Theta method forecast
tsdisplay Time series display
is.forecast Is an object a particular forecast type?
forecast.Arima Forecasting using ARIMA or ARFIMA models
fortify.forecast Fortify a forecast object to data.frame for ggplot
plot.mforecast Multivariate forecast plot
plot.ets Plot components from ETS model
naive Naive forecasts
na.interp Interpolate missing values in a time series
rwf Random Walk Forecast
arimaorder Return the order of an ARIMA or ARFIMA model
is.ets Is an object a particular model type?
plot.Arima Plot characteristic roots from ARIMA model
seasonplot Seasonal plot
seasadj Seasonal adjustment
seasonaldummy Seasonal dummy variables
plot.forecast Forecast plot
forecast.HoltWinters Forecasting using Holt-Winters objects
forecast.lm Forecast a linear model with possible time series components
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
ndiffs Number of differences required for a stationary series
ets Exponential smoothing state space model
taylor Half-hourly electricity demand
autoplot.stl ggplot STL object
gas Australian monthly gas production
croston Forecasts for intermittent demand using Croston's method
simulate.ets Simulation from a time series model
BoxCox Box Cox Transformation
plot.bats Plot components from BATS model
geom_forecast Forecast plot
sindexf Forecast seasonal index
bizdays Number of trading days in each season
easter Easter holidays in each season
ggmonthplot Create a seasonal subseries ggplot
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
mforecast Forecasting time series
autoplot.decomposed.ts ggplot of a decomposed time series object
forecast.ets Forecasting using ETS models
monthdays Number of days in each season
auto.arima Fit best ARIMA model to univariate time series
tsoutliers Identify and replace outliers in a time series
is.constant Is an object constant?
getResponse Get response variable from time series model.
forecast.bats Forecasting using BATS and TBATS models
forecast.StructTS Forecasting using Structural Time Series models
forecast.stl Forecasting using stl objects
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
wineind Australian total wine sales
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation
tsclean Identify and replace outliers and missing values in a time series
accuracy Accuracy measures for forecast model
ma Moving-average smoothing
autoplot.ts Automatically create a ggplot for time series objects
findfrequency Find dominant frequency of a time series
BoxCox.lambda Automatic selection of Box Cox transformation parameter
meanf Mean Forecast
ses Exponential smoothing forecasts
forecast.mlm Forecast a multiple linear model with possible time series components
dm.test Diebold-Mariano test for predictive accuracy
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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 2016-04-14 05:35:26 UTC; hyndman
Repository CRAN
Date/Publication 2016-04-14 14:53:40

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