forecast v2.05


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


Date 2010-05-11
LazyData yes
LazyLoad yes
License GPL (>= 2)
Packaged 2010-05-11 03:45:06 UTC; hyndman
Repository CRAN
Date/Publication 2010-05-11 05:33:02

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