forecast v2.06


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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
gas Australian monthly gas production
Arima Fit ARIMA model to univariate time series
forecast.HoltWinters Forecasting using Holt-Winters objects
ndiffs Number of differences
rwf Random Walk Forecast
arfima Fit a fractionally differenced ARFIMA model
seasonaldummy Seasonal dummy variables
plot.ets Plot components from ETS model
fitted.Arima One-step in-sample forecasts using ARIMA models
simulate.ets Simulation from a time series model
auto.arima Fit best ARIMA model to univariate time series
plot.forecast Forecast plot
na.interp Interpolate missing values in a time series
splinef Cubic Spline Forecast
logLik.ets Log-Likelihood of an ets object
ets Exponential smoothing state space model
woolyrnq Quarterly production of woollen yarn in Australia
seasadj Seasonal adjustment
monthdays Number of days in each season
BoxCox Box Cox Transformation
forecast.StructTS Forecasting using Structural Time Series models
seasonplot Seasonal plot
ses Exponential smoothing forecasts
thetaf Theta method forecast
arima.errors ARIMA errors
gold Daily morning gold prices
sindexf Forecast seasonal index
forecast Forecasting time series
dm.test Diebold-Mariano test for predictive accuracy
meanf Mean Forecast
croston Forecasts for intermittent demand using Croston's method
forecast.Arima Forecasting using ARIMA or ARFIMA models
tsdisplay Time series display
accuracy Accuracy measures for forecast model
forecast.ets Forecasting using ETS models
wineind Australian total wine sales
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Last month downloads


Date 2010-07-29
LazyData yes
LazyLoad yes
License GPL (>= 2)
Packaged 2010-07-29 06:44:30 UTC; hyndman
Repository CRAN
Date/Publication 2010-07-29 09:13:38

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