forecast v3.02


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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
plot.forecast Forecast plot
auto.arima Fit best ARIMA model to univariate time series
forecast.Arima Forecasting using ARIMA or ARFIMA models
Acf (Partial) Autocorrelation Function Estimation
BoxCox Box Cox Transformation
wineind Australian total wine sales
ndiffs Number of differences required for a stationary series
forecast.lm Forecast a linear model with possible time series components
arfima Fit a fractionally differenced ARFIMA model
na.interp Interpolate missing values in a time series
arima.errors ARIMA errors
ma Moving-average smoothing
forecast Forecasting time series
gas Australian monthly gas production
fitted.Arima One-step in-sample forecasts using ARIMA models
gold Daily morning gold prices
CV Cross-validation statistic
dm.test Diebold-Mariano test for predictive accuracy
plot.ets Plot components from ETS model
forecast.HoltWinters Forecasting using Holt-Winters objects
meanf Mean Forecast
forecast.StructTS Forecasting using Structural Time Series models
logLik.ets Log-Likelihood of an ets object
seasadj Seasonal adjustment
splinef Cubic Spline Forecast
rwf Random Walk Forecast
ets Exponential smoothing state space model
naive Naive forecasts
seasonaldummy Seasonal dummy variables
forecast.ets Forecasting using ETS models
seasonplot Seasonal plot
monthdays Number of days in each season
ses Exponential smoothing forecasts
forecast.stl Forecasting using stl objects
woolyrnq Quarterly production of woollen yarn in Australia
croston Forecasts for intermittent demand using Croston's method
simulate.ets Simulation from a time series model
accuracy Accuracy measures for forecast model
BoxCox.lambda Automatic selection of Box Cox transformation parameter
tslm Fit a linear model with time series components
taylor Half-hourly electricity demand
Arima Fit ARIMA model to univariate time series
thetaf Theta method forecast
subset.ts Subsetting a time series
sindexf Forecast seasonal index
tsdisplay Time series display
decompose Classical Seasonal Decomposition by Moving Averages
dshw Double-Seasonal Holt-Winters Forecasting
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Last month downloads


Date 2011-08-25
LazyData yes
LazyLoad yes
License GPL (>= 2)
Packaged 2011-08-25 12:18:18 UTC; Rob
Repository CRAN
Date/Publication 2011-08-25 12:28:38

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