forecast v5.4


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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
fitted.Arima One-step in-sample forecasts using ARIMA models
logLik.ets Log-Likelihood of an ets object
BoxCox.lambda Automatic selection of Box Cox transformation parameter
gold Daily morning gold prices
Acf (Partial) Autocorrelation Function Estimation
forecast.lm Forecast a linear model with possible time series components
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)
arfima Fit a fractionally differenced ARFIMA model
arima.errors ARIMA errors
seasadj Seasonal adjustment
plot.forecast Forecast plot
tsdisplay Time series display
nnetar Neural Network Time Series Forecasts
BoxCox Box Cox Transformation
forecast.ets Forecasting using ETS models
simulate.ets Simulation from a time series model
forecast.StructTS Forecasting using Structural Time Series models
splinef Cubic Spline Forecast
seasonplot Seasonal plot
getResponse Get response variable from time series model.
easter Easter holidays in each season
ma Moving-average smoothing
naive Naive forecasts
tslm Fit a linear model with time series components
tsoutliers Identify and replace outliers in a time series
woolyrnq Quarterly production of woollen yarn in Australia
dm.test Diebold-Mariano test for predictive accuracy
wineind Australian total wine sales
sindexf Forecast seasonal index
ets Exponential smoothing state space model
forecast.Arima Forecasting using ARIMA or ARFIMA models
ndiffs Number of differences required for a stationary series
arimaorder Return the order of an ARIMA or ARFIMA model
taylor Half-hourly electricity demand
plot.ets Plot components from ETS model
CV Cross-validation statistic
forecast.HoltWinters Forecasting using Holt-Winters objects
plot.bats Plot components from BATS model
monthdays Number of days in each season
seasonaldummy Seasonal dummy variables
rwf Random Walk Forecast
dshw Double-Seasonal Holt-Winters Forecasting
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
ses Exponential smoothing forecasts
tbats.components Extract components of a TBATS model
croston Forecasts for intermittent demand using Croston's method
gas Australian monthly gas production
msts Multi-Seasonal Time Series
accuracy Accuracy measures for forecast model
Arima Fit ARIMA model to univariate time series
bizdays Number of trading days in each season
forecast.bats Forecasting using BATS and TBATS models
forecast.stl Forecasting using stl objects
auto.arima Fit best ARIMA model to univariate time series
meanf Mean Forecast
subset.ts Subsetting a time series
forecast Forecasting time series
thetaf Theta method forecast
tsclean Identify and replace outliers and missing values in a time series
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LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
Packaged 2014-05-07 23:17:30 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2014-05-08 07:19:18

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