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forecast (version 3.12)

Forecasting functions for time series

Description

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

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Version

Install

install.packages('forecast')

Monthly Downloads

137,850

Version

3.12

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

November 16th, 2011

Functions in forecast (3.12)

forecast.ets

Forecasting using ETS models
plot.ets

Plot components from ETS model
accuracy

Accuracy measures for forecast model
CV

Cross-validation statistic
gold

Daily morning gold prices
BoxCox.lambda

Automatic selection of Box Cox transformation parameter
ets

Exponential smoothing state space model
croston

Forecasts for intermittent demand using Croston's method
meanf

Mean Forecast
forecast.HoltWinters

Forecasting using Holt-Winters objects
BoxCox

Box Cox Transformation
Arima

Fit ARIMA model to univariate time series
Acf

(Partial) Autocorrelation Function Estimation
auto.arima

Fit best ARIMA model to univariate time series
plot.forecast

Forecast plot
forecast.Arima

Forecasting using ARIMA or ARFIMA models
arfima

Fit a fractionally differenced ARFIMA model
simulate.ets

Simulation from a time series model
logLik.ets

Log-Likelihood of an ets object
dshw

Double-Seasonal Holt-Winters Forecasting
dm.test

Diebold-Mariano test for predictive accuracy
ma

Moving-average smoothing
forecast

Forecasting time series
forecast.StructTS

Forecasting using Structural Time Series models
fitted.Arima

One-step in-sample forecasts using ARIMA models
forecast.stl

Forecasting using stl objects
seasonaldummy

Seasonal dummy variables
na.interp

Interpolate missing values in a time series
naive

Naive forecasts
taylor

Half-hourly electricity demand
subset.ts

Subsetting a time series
splinef

Cubic Spline Forecast
arima.errors

ARIMA errors
rwf

Random Walk Forecast
sindexf

Forecast seasonal index
ses

Exponential smoothing forecasts
seasonplot

Seasonal plot
wineind

Australian total wine sales
tslm

Fit a linear model with time series components
tsdisplay

Time series display
woolyrnq

Quarterly production of woollen yarn in Australia
seasadj

Seasonal adjustment
decompose

Classical Seasonal Decomposition by Moving Averages
monthdays

Number of days in each season
thetaf

Theta method forecast
ndiffs

Number of differences required for a stationary series
gas

Australian monthly gas production
forecast.lm

Forecast a linear model with possible time series components