forecast v3.18

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by Rob Hyndman

Forecasting functions for time series

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

Date 2012-02-17
LinkingTo Rcpp, RcppArmadillo
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
URL http://robjhyndman.com/software/forecast/
Packaged 2012-02-17 03:02:30 UTC; hyndman
Repository CRAN
Date/Publication 2012-02-17 07:38:40

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