forecast v6.2


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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
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
plot.Arima Plot characteristic roots from ARIMA model
accuracy Accuracy measures for forecast model
bizdays Number of trading days in each season
forecast.ets Forecasting using ETS models
dm.test Diebold-Mariano test for predictive accuracy
dshw Double-Seasonal Holt-Winters Forecasting
nnetar Neural Network Time Series Forecasts
meanf Mean Forecast
getResponse Get response variable from time series model.
na.interp Interpolate missing values in a time series
ses Exponential smoothing forecasts
ets Exponential smoothing state space model
BoxCox Box Cox Transformation
Arima Fit ARIMA model to univariate time series
plot.forecast Forecast plot
msts Multi-Seasonal Time Series
CV Cross-validation statistic
BoxCox.lambda Automatic selection of Box Cox transformation parameter
arimaorder Return the order of an ARIMA or ARFIMA model
seasonplot Seasonal plot
ma Moving-average smoothing
plot.bats Plot components from BATS model
forecast.lm Forecast a linear model with possible time series components
forecast.bats Forecasting using BATS and TBATS models
arima.errors ARIMA errors
splinef Cubic Spline Forecast
easter Easter holidays in each season
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
auto.arima Fit best ARIMA model to univariate time series
thetaf Theta method forecast
naive Naive forecasts
forecast.stl Forecasting using stl objects
tsclean Identify and replace outliers and missing values in a time series
monthdays Number of days in each season
forecast.StructTS Forecasting using Structural Time Series models
forecast Forecasting time series
plot.ets Plot components from ETS model
sindexf Forecast seasonal index
gold Daily morning gold prices
Acf (Partial) Autocorrelation Function Estimation
ndiffs Number of differences required for a stationary series
tsoutliers Identify and replace outliers in a time series
croston Forecasts for intermittent demand using Croston's method
tslm Fit a linear model with time series components
findfrequency Find dominant frequency of a time series
simulate.ets Simulation from a time series model
tbats.components Extract components of a TBATS model
seasadj Seasonal adjustment
taylor Half-hourly electricity demand
seasonaldummy Seasonal dummy variables
rwf Random Walk Forecast
subset.ts Subsetting a time series
gas Australian monthly gas production
fitted.Arima One-step in-sample forecasts using ARIMA models
logLik.ets Log-Likelihood of an ets object
arfima Fit a fractionally differenced ARFIMA model
tsdisplay Time series display
woolyrnq Quarterly production of woollen yarn in Australia
forecast.HoltWinters Forecasting using Holt-Winters objects
forecast.Arima Forecasting using ARIMA or ARFIMA models
wineind Australian total wine sales
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LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
NeedsCompilation yes
Packaged 2015-10-19 22:52:46 UTC; hyndman
Repository CRAN
Date/Publication 2015-10-20 08:06:46

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