# forecast v5.2

0

Monthly downloads

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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## Details

LinkingTo | Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35) |

LazyData | yes |

ByteCompile | TRUE |

License | GPL (>= 2) |

URL | http://robjhyndman.com/software/forecast/ |

Packaged | 2014-02-24 06:21:51 UTC; hyndman |

NeedsCompilation | yes |

Repository | CRAN |

Date/Publication | 2014-02-24 08:05:42 |

depends | base (>= 3.0.2) , graphics , R (>= 3.0.2) , stats , timeDate , zoo |

imports | colorspace , fracdiff , nnet , parallel , Rcpp (>= 0.11.0) , RcppArmadillo (>= 0.2.35) , tseries |

suggests | Rmalschains |

Contributors | Rob Hyndman |

#### Include our badge in your README

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