forecast package - RDocumentation

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#forecast

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).

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Version

Install

install.packages('forecast')

Monthly Downloads

213,748

Version

7.3

License

GPL (>= 2)

Issues

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Repository

http://github.com/robjhyndman/forecast

Maintainer

Last Published

October 13th, 2016

Functions in forecast (7.3)

autoplot.decomposed.ts

ggplot of a decomposed time series object

ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation

Fit best ARIMA model to univariate time series

Accuracy measures for forecast model

Return the order of an ARIMA or ARFIMA model

Fit a fractionally differenced ARFIMA model

(Partial) Autocorrelation and Cross-Correlation Function Estimation

Fit ARIMA model to univariate time series

ggplot STL object

Cross-validation statistic

Diebold-Mariano test for predictive accuracy

Number of trading days in each season

Double-Seasonal Holt-Winters Forecasting

Box Cox Transformation

Automatically create a ggplot for time series objects

Easter holidays in each season

Automatic selection of Box Cox transformation parameter

BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Forecasts for intermittent demand using Croston's method

h-step in-sample forecasts using tbats models

h-step in-sample forecasts using ARIMA models

Exponential smoothing state space model

Forecasting using ETS models

h-step in-sample forecasts using bats models

Find dominant frequency of a time series

Forecasting using BATS and TBATS models

h-step in-sample forecasts using ets models

Forecasting using ARIMA or ARFIMA models

h-step in-sample forecasts using nnetar models

forecast.StructTS

Forecasting using Structural Time Series models

Forecast a multiple linear model with possible time series components

forecast.HoltWinters

Forecasting using Holt-Winters objects

Forecast a linear model with possible time series components

Australian monthly gas production

Forecasting using stl objects

forecast.nnetar

Forecasting using neural network models

fortify.forecast

Fortify a forecast object to data.frame for ggplot

Forecasting time series

Create a seasonal subseries ggplot

Time series lag ggplots

Is an object constant?

Daily morning gold prices

Is an object a particular model type?

Interpolate missing values in a time series

Naive and Random Walk Forecasts

Is an object a particular forecast type?

Get response variable from time series model.

Moving-average smoothing

Log-Likelihood of an ets object

Plot characteristic roots from ARIMA model

Australian total wine sales

Seasonal dummy variables

Plot components from BATS model

Seasonal adjustment

Quarterly production of woollen yarn in Australia

Forecast seasonal index

Simulation from a time series model

Half-hourly electricity demand

tbats.components

Extract components of a TBATS model

Plot components from ETS model

Multivariate forecast plot

Identify and replace outliers in a time series

Number of days in each season

Fit a linear model with time series components

Multi-Seasonal Time Series

Subsetting a time series

Cubic Spline Forecast

TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Theta method forecast

Identify and replace outliers and missing values in a time series

Number of differences required for a stationary series

Time series display

Neural Network Time Series Forecasts

Forecasting time series

Exponential smoothing forecasts