forecast v3.20

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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
auto.arima Fit best ARIMA model to univariate time series
logLik.ets Log-Likelihood of an ets object
dshw Double-Seasonal Holt-Winters Forecasting
gold Daily morning gold prices
ma Moving-average smoothing
seasonplot Seasonal plot
na.interp Interpolate missing values in a time series
BoxCox.lambda Automatic selection of Box Cox transformation parameter
subset.ts Subsetting a time series
thetaf Theta method forecast
dm.test Diebold-Mariano test for predictive accuracy
gas Australian monthly gas production
ndiffs Number of differences required for a stationary series
sindexf Forecast seasonal index
arima.errors ARIMA errors
arfima Fit a fractionally differenced ARFIMA model
naive Naive forecasts
forecast.ets Forecasting using ETS models
croston Forecasts for intermittent demand using Croston's method
taylor Half-hourly electricity demand
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
forecast.lm Forecast a linear model with possible time series components
rwf Random Walk Forecast
forecast Forecasting time series
meanf Mean Forecast
fitted.Arima One-step in-sample forecasts using ARIMA models
tsdisplay Time series display
msts Multi-Seasonal Time Series
plot.forecast Forecast plot
forecast.stl Forecasting using stl objects
accuracy Accuracy measures for forecast model
simulate.ets Simulation from a time series model
wineind Australian total wine sales
forecast.Arima Forecasting using ARIMA or ARFIMA models
woolyrnq Quarterly production of woollen yarn in Australia
seasonaldummy Seasonal dummy variables
Acf (Partial) Autocorrelation Function Estimation
tslm Fit a linear model with time series components
CV Cross-validation statistic
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
ets Exponential smoothing state space model
forecast.HoltWinters Forecasting using Holt-Winters objects
seasadj Seasonal adjustment
Arima Fit ARIMA model to univariate time series
forecast.StructTS Forecasting using Structural Time Series models
splinef Cubic Spline Forecast
plot.ets Plot components from ETS model
BoxCox Box Cox Transformation
forecast.bats Forecasting using BATS and TBATS models
ses Exponential smoothing forecasts
monthdays Number of days in each season
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Details

Date 2012-04-02
LinkingTo Rcpp, RcppArmadillo
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
URL http://robjhyndman.com/software/forecast/
Packaged 2012-04-02 03:22:03 UTC; hyndman
Repository CRAN
Date/Publication 2012-04-02 06:49:12

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