forecast v7.0


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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
autoplot.stl ggplot STL object
ndiffs Number of differences required for a stationary series
CV Cross-validation statistic
ets Exponential smoothing state space model
forecast.bats Forecasting using BATS and TBATS models
logLik.ets Log-Likelihood of an ets object
auto.arima Fit best ARIMA model to univariate time series
autoplot.acf ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation
forecast.Arima Forecasting using ARIMA or ARFIMA models
taylor Half-hourly electricity demand
woolyrnq Quarterly production of woollen yarn in Australia
thetaf Theta method forecast
tsclean Identify and replace outliers and missing values in a time series
accuracy Accuracy measures for forecast model
fitted.Arima One-step in-sample forecasts using ARIMA models
is.ets Is an object a particular model type?
forecast.lm Forecast a linear model with possible time series components
tsdisplay Time series display
msts Multi-Seasonal Time Series
is.forecast Is an object a particular forecast type?
arfima Fit a fractionally differenced ARFIMA model
BoxCox Box Cox Transformation
plot.bats Plot components from BATS model
findfrequency Find dominant frequency of a time series
ggmonthplot Create a seasonal subseries ggplot
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
autoplot.decomposed.ts ggplot of a decomposed time series object
plot.forecast Forecast plot
ses Exponential smoothing forecasts
forecast.HoltWinters Forecasting using Holt-Winters objects
forecast.StructTS Forecasting using Structural Time Series models
mforecast Forecasting time series
plot.ets Plot components from ETS model
seasonplot Seasonal plot
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
gold Daily morning gold prices
rwf Random Walk Forecast
splinef Cubic Spline Forecast
BoxCox.lambda Automatic selection of Box Cox transformation parameter
autoplot.ts Automatically create a ggplot for time series objects
bizdays Number of trading days in each season
na.interp Interpolate missing values in a time series
croston Forecasts for intermittent demand using Croston's method
seasadj Seasonal adjustment
forecast.mlm Forecast a multiple linear model with possible time series components
tslm Fit a linear model with time series components
dshw Double-Seasonal Holt-Winters Forecasting
is.constant Is an object constant?
meanf Mean Forecast
wineind Australian total wine sales
Arima Fit ARIMA model to univariate time series
gas Australian monthly gas production
getResponse Get response variable from time series model.
ma Moving-average smoothing
plot.mforecast Multivariate forecast plot
sindexf Forecast seasonal index
forecast.ets Forecasting using ETS models
plot.Arima Plot characteristic roots from ARIMA model
nnetar Neural Network Time Series Forecasts
arimaorder Return the order of an ARIMA or ARFIMA model
fortify.forecast Fortify a forecast object to data.frame for ggplot
simulate.ets Simulation from a time series model
tbats.components Extract components of a TBATS model
tsoutliers Identify and replace outliers in a time series
arima.errors ARIMA errors
easter Easter holidays in each season
forecast Forecasting time series
forecast.stl Forecasting using stl objects
geom_forecast Forecast plot
seasonaldummy Seasonal dummy variables
subset.ts Subsetting a time series
naive Naive forecasts
Acf (Partial) Autocorrelation and Cross-Correlation Function Estimation
monthdays Number of days in each season
dm.test Diebold-Mariano test for predictive accuracy
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LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
NeedsCompilation yes
Packaged 2016-04-03 22:35:26 UTC; hyndman
Repository CRAN
Date/Publication 2016-04-04 01:21:21

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