forecast v6.1

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

Date 2015-05-11
LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
BugReports https://github.com/robjhyndman/forecast/issues
License GPL (>= 2)
URL http://github.com/robjhyndman/forecast
NeedsCompilation yes
Packaged 2015-05-12 09:56:10 UTC; hyndman
Repository CRAN
Date/Publication 2015-05-12 12:37:47

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