forecast v4.8

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

Date 2013-09-30
LinkingTo Rcpp, RcppArmadillo
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
URL http://robjhyndman.com/software/forecast/
Packaged 2013-09-30 00:14:10 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2013-09-30 08:07:30

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