forecast v2.09


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


Date 2010-10-15
LazyData yes
LazyLoad yes
License GPL (>= 2)
Packaged 2010-10-15 01:14:18 UTC; hyndman
Repository CRAN
Date/Publication 2010-10-15 10:06:33

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