forecast v3.11


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


Date 2011-11-02
LazyData yes
License GPL (>= 2)
Packaged 2011-11-02 03:52:44 UTC; hyndman
Repository CRAN
Date/Publication 2011-11-02 17:25:47

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