forecast v4.02

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by Rob Hyndman

Forecasting functions for time series and linear models

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

Date 2013-03-06
LinkingTo Rcpp, RcppArmadillo
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
URL http://robjhyndman.com/software/forecast/
Packaged 2013-03-06 03:09:45 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2013-03-06 07:17:01

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