forecast v5.8


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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
forecast.ets Forecasting using ETS models
meanf Mean Forecast
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
ets Exponential smoothing state space model
arimaorder Return the order of an ARIMA or ARFIMA model
CV Cross-validation statistic
splinef Cubic Spline Forecast
tslm Fit a linear model with time series components
forecast.HoltWinters Forecasting using Holt-Winters objects
subset.ts Subsetting a time series
thetaf Theta method forecast
taylor Half-hourly electricity demand
logLik.ets Log-Likelihood of an ets object
msts Multi-Seasonal Time Series
auto.arima Fit best ARIMA model to univariate time series
plot.ets Plot components from ETS model
tsclean Identify and replace outliers and missing values in a time series
na.interp Interpolate missing values in a time series
Arima Fit ARIMA model to univariate time series
nnetar Neural Network Time Series Forecasts
tsoutliers Identify and replace outliers in a time series
forecast Forecasting time series
ses Exponential smoothing forecasts
BoxCox.lambda Automatic selection of Box Cox transformation parameter
forecast.stl Forecasting using stl objects
croston Forecasts for intermittent demand using Croston's method
woolyrnq Quarterly production of woollen yarn in Australia
tsdisplay Time series display
simulate.ets Simulation from a time series model
wineind Australian total wine sales
gas Australian monthly gas production
tbats.components Extract components of a TBATS model
arima.errors ARIMA errors
seasonaldummy Seasonal dummy variables
forecast.Arima Forecasting using ARIMA or ARFIMA models
gold Daily morning gold prices
ma Moving-average smoothing
plot.forecast Forecast plot
naive Naive forecasts
ndiffs Number of differences required for a stationary series
forecast.StructTS Forecasting using Structural Time Series models
easter Easter holidays in each season
arfima Fit a fractionally differenced ARFIMA model
plot.bats Plot components from BATS model
sindexf Forecast seasonal index
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
getResponse Get response variable from time series model.
fitted.Arima One-step in-sample forecasts using ARIMA models
seasonplot Seasonal plot
bizdays Number of trading days in each season
findfrequency Find dominant frequency of a time series
dm.test Diebold-Mariano test for predictive accuracy
seasadj Seasonal adjustment
monthdays Number of days in each season
accuracy Accuracy measures for forecast model
BoxCox Box Cox Transformation
rwf Random Walk Forecast
Acf (Partial) Autocorrelation Function Estimation
forecast.bats Forecasting using BATS and TBATS models
forecast.lm Forecast a linear model with possible time series components
dshw Double-Seasonal Holt-Winters Forecasting
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Date 2015-01-06
LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
Packaged 2015-01-06 10:14:10 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2015-01-06 17:30:51

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