forecast v5.6


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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
dm.test Diebold-Mariano test for predictive accuracy
bizdays Number of trading days in each season
forecast.Arima Forecasting using ARIMA or ARFIMA models
forecast Forecasting time series
forecast.ets Forecasting using ETS models
msts Multi-Seasonal Time Series
nnetar Neural Network Time Series Forecasts
logLik.ets Log-Likelihood of an ets object
arfima Fit a fractionally differenced ARFIMA model
subset.ts Subsetting a time series
wineind Australian total wine sales
plot.bats Plot components from BATS model
tsoutliers Identify and replace outliers in a time series
easter Easter holidays in each season
CV Cross-validation statistic
plot.forecast Forecast plot
na.interp Interpolate missing values in a time series
rwf Random Walk Forecast
tslm Fit a linear model with time series components
auto.arima Fit best ARIMA model to univariate time series
dshw Double-Seasonal Holt-Winters Forecasting
naive Naive forecasts
tbats.components Extract components of a TBATS model
Acf (Partial) Autocorrelation Function Estimation
BoxCox Box Cox Transformation
thetaf Theta method forecast
simulate.ets Simulation from a time series model
bats BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
meanf Mean Forecast
forecast.StructTS Forecasting using Structural Time Series models
seasonaldummy Seasonal dummy variables
ets Exponential smoothing state space model
tsclean Identify and replace outliers and missing values in a time series
arimaorder Return the order of an ARIMA or ARFIMA model
ses Exponential smoothing forecasts
forecast.stl Forecasting using stl objects
plot.ets Plot components from ETS model
accuracy Accuracy measures for forecast model
fitted.Arima One-step in-sample forecasts using ARIMA models
gas Australian monthly gas production
tbats TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
sindexf Forecast seasonal index
taylor Half-hourly electricity demand
arima.errors ARIMA errors
forecast.HoltWinters Forecasting using Holt-Winters objects
getResponse Get response variable from time series model.
BoxCox.lambda Automatic selection of Box Cox transformation parameter
croston Forecasts for intermittent demand using Croston's method
splinef Cubic Spline Forecast
gold Daily morning gold prices
ndiffs Number of differences required for a stationary series
forecast.bats Forecasting using BATS and TBATS models
ma Moving-average smoothing
tsdisplay Time series display
woolyrnq Quarterly production of woollen yarn in Australia
monthdays Number of days in each season
forecast.lm Forecast a linear model with possible time series components
seasonplot Seasonal plot
seasadj Seasonal adjustment
Arima Fit ARIMA model to univariate time series
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LinkingTo Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35)
LazyData yes
ByteCompile TRUE
License GPL (>= 2)
Packaged 2014-09-24 00:03:49 UTC; hyndman
NeedsCompilation yes
Repository CRAN
Date/Publication 2014-09-24 06:54:26

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