forecast v2.11


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


Date 2010-11-04
LazyData yes
LazyLoad yes
License GPL (>= 2)
Packaged 2010-11-04 01:26:55 UTC; hyndman
Repository CRAN
Date/Publication 2010-11-04 07:15:09

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