forecast v5.0

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

Date 2014-01-??
LinkingTo Rcpp, RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
URL http://robjhyndman.com/software/forecast/
Packaged 2014-01-17 01:56:12 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2014-01-17 06:13:02

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