forecast v3.12

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by Rob Hyndman

Forecasting functions for time series

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

Functions in forecast

Name Description
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
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Details

Date 2011-11-16
LazyData yes
License GPL (>= 2)
URL http://robjhyndman.com/software/forecast/
Packaged 2011-11-16 02:17:25 UTC; hyndman
Repository CRAN
Date/Publication 2011-11-16 12:16:43

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