forecast v2.12

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

Date 2011-01-17
LazyData yes
LazyLoad yes
License GPL (>= 2)
URL http://robjhyndman.com/software/forecast/
Packaged 2011-01-19 05:06:00 UTC; hyndman
Repository CRAN
Date/Publication 2011-01-19 08:21:14

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