forecast v2.07


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


Date 2010-09-09
LazyData yes
LazyLoad yes
License GPL (>= 2)
Packaged 2010-09-09 05:01:35 UTC; hyndman
Repository CRAN
Date/Publication 2010-09-09 12:08:00

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