forecast v5.5


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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
plot.forecast Forecast plot
monthdays Number of days in each season
Arima Fit ARIMA model to univariate time series
BoxCox.lambda Automatic selection of Box Cox transformation parameter
rwf Random Walk Forecast
tsoutliers Identify and replace outliers in a time series
arfima Fit a fractionally differenced ARFIMA model
getResponse Get response variable from time series model.
tsclean Identify and replace outliers and missing values in a time series
meanf Mean Forecast
forecast.StructTS Forecasting using Structural Time Series models
seasadj Seasonal adjustment
msts Multi-Seasonal Time Series
wineind Australian total wine sales
gold Daily morning gold prices
tsdisplay Time series display
seasonaldummy Seasonal dummy variables
simulate.ets Simulation from a time series model
dm.test Diebold-Mariano test for predictive accuracy
ses Exponential smoothing forecasts
na.interp Interpolate missing values in a time series
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
forecast.stl Forecasting using stl objects
arima.errors ARIMA errors
subset.ts Subsetting a time series
tbats.components Extract components of a TBATS model
forecast.ets Forecasting using ETS models
logLik.ets Log-Likelihood of an ets object
plot.ets Plot components from ETS model
dshw Double-Seasonal Holt-Winters Forecasting
bizdays Number of trading days in each season
forecast.lm Forecast a linear model with possible time series components
plot.bats Plot components from BATS model
naive Naive forecasts
gas Australian monthly gas production
fitted.Arima One-step in-sample forecasts using ARIMA models
thetaf Theta method forecast
ndiffs Number of differences required for a stationary series
forecast.Arima Forecasting using ARIMA or ARFIMA models
ma Moving-average smoothing
arimaorder Return the order of an ARIMA or ARFIMA model
CV Cross-validation statistic
forecast Forecasting time series
BoxCox Box Cox Transformation
woolyrnq Quarterly production of woollen yarn in Australia
nnetar Neural Network Time Series Forecasts
croston Forecasts for intermittent demand using Croston's method
taylor Half-hourly electricity demand
auto.arima Fit best ARIMA model to univariate time series
sindexf Forecast seasonal index
splinef Cubic Spline Forecast
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
easter Easter holidays in each season
tslm Fit a linear model with time series components
Acf (Partial) Autocorrelation Function Estimation
accuracy Accuracy measures for forecast model
ets Exponential smoothing state space model
forecast.bats Forecasting using BATS and TBATS models
forecast.HoltWinters Forecasting using Holt-Winters objects
seasonplot Seasonal plot
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LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
Packaged 2014-08-12 04:21:35 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2014-08-12 06:52:24

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