forecast v5.2


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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
forecast.lm Forecast a linear model with possible time series components
fitted.Arima One-step in-sample forecasts using ARIMA models
BoxCox Box Cox Transformation
CV Cross-validation statistic
Arima Fit ARIMA model to univariate time series
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)
thetaf Theta method forecast
simulate.ets Simulation from a time series model
accuracy Accuracy measures for forecast model
getResponse Get response variable from time series model.
msts Multi-Seasonal Time Series
monthdays Number of days in each season
dshw Double-Seasonal Holt-Winters Forecasting
meanf Mean Forecast
easter Easter holidays in each season
seasadj Seasonal adjustment
ma Moving-average smoothing
taylor Half-hourly electricity demand
forecast.stl Forecasting using stl objects
seasonplot Seasonal plot
plot.forecast Forecast plot
forecast.bats Forecasting using BATS and TBATS models
gas Australian monthly gas production
tslm Fit a linear model with time series components
sindexf Forecast seasonal index
arima.errors ARIMA errors
tbats.components Extract components of a TBATS model
forecast.HoltWinters Forecasting using Holt-Winters objects
nnetar Neural Network Time Series Forecasts
ses Exponential smoothing forecasts
ndiffs Number of differences required for a stationary series
forecast.Arima Forecasting using ARIMA or ARFIMA models
arfima Fit a fractionally differenced ARFIMA model
bizdays Number of trading days in each season
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
BoxCox.lambda Automatic selection of Box Cox transformation parameter
dm.test Diebold-Mariano test for predictive accuracy
plot.bats Plot components from BATS model
croston Forecasts for intermittent demand using Croston's method
tsclean Identify and replace outliers and missing values in a time series
subset.ts Subsetting a time series
naive Naive forecasts
plot.ets Plot components from ETS model
forecast.ets Forecasting using ETS models
ets Exponential smoothing state space model
seasonaldummy Seasonal dummy variables
woolyrnq Quarterly production of woollen yarn in Australia
tsdisplay Time series display
Acf (Partial) Autocorrelation Function Estimation
logLik.ets Log-Likelihood of an ets object
na.interp Interpolate missing values in a time series
splinef Cubic Spline Forecast
rwf Random Walk Forecast
tsoutliers Identify and replace outliers in a time series
wineind Australian total wine sales
gold Daily morning gold prices
forecast Forecasting time series
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LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
Packaged 2014-02-24 06:21:51 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2014-02-24 08:05:42

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