forecast v6.0


Monthly downloads



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
ma Moving-average smoothing
gold Daily morning gold prices
monthdays Number of days in each season
BoxCox.lambda Automatic selection of Box Cox transformation parameter
thetaf Theta method forecast
tsdisplay Time series display
forecast.stl Forecasting using stl objects
dm.test Diebold-Mariano test for predictive accuracy
tsclean Identify and replace outliers and missing values in a time series
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
Arima Fit ARIMA model to univariate time series
meanf Mean Forecast
sindexf Forecast seasonal index
nnetar Neural Network Time Series Forecasts
easter Easter holidays in each season
simulate.ets Simulation from a time series model
forecast.Arima Forecasting using ARIMA or ARFIMA models
findfrequency Find dominant frequency of a time series
splinef Cubic Spline Forecast
forecast.ets Forecasting using ETS models
plot.Arima Plot characteristic roots from ARIMA model
tbats.components Extract components of a TBATS model
ets Exponential smoothing state space model
plot.ets Plot components from ETS model
forecast.HoltWinters Forecasting using Holt-Winters objects
tslm Fit a linear model with time series components
subset.ts Subsetting a time series
arimaorder Return the order of an ARIMA or ARFIMA model
plot.forecast Forecast plot
croston Forecasts for intermittent demand using Croston's method
BoxCox Box Cox Transformation
CV Cross-validation statistic
forecast.StructTS Forecasting using Structural Time Series models
bizdays Number of trading days in each season
seasadj Seasonal adjustment
ses Exponential smoothing forecasts
forecast.lm Forecast a linear model with possible time series components
fitted.Arima One-step in-sample forecasts using ARIMA models
rwf Random Walk Forecast
dshw Double-Seasonal Holt-Winters Forecasting
arfima Fit a fractionally differenced ARFIMA model
arima.errors ARIMA errors
wineind Australian total wine sales
woolyrnq Quarterly production of woollen yarn in Australia
tsoutliers Identify and replace outliers in a time series
ndiffs Number of differences required for a stationary series
getResponse Get response variable from time series model.
plot.bats Plot components from BATS model
logLik.ets Log-Likelihood of an ets object
naive Naive forecasts
Acf (Partial) Autocorrelation Function Estimation
msts Multi-Seasonal Time Series
taylor Half-hourly electricity demand
seasonaldummy Seasonal dummy variables
auto.arima Fit best ARIMA model to univariate time series
forecast.bats Forecasting using BATS and TBATS models
seasonplot Seasonal plot
gas Australian monthly gas production
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
na.interp Interpolate missing values in a time series
forecast Forecasting time series
accuracy Accuracy measures for forecast model
No Results!

Last month downloads


Date 2015-05-09
LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
NeedsCompilation yes
Packaged 2015-05-09 10:55:46 UTC; hyndman
Repository CRAN
Date/Publication 2015-05-09 23:14:04

Include our badge in your README