forecast v2.00


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


Date 2009-09-07
LazyData yes
LazyLoad yes
License GPL (>= 2)
Packaged 2009-09-07 04:21:13 UTC; hyndman
Repository CRAN
Date/Publication 2009-09-07 09:38:06

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