forecast v3.18
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by Rob Hyndman
Forecasting functions for time series
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 | |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
| gas | Australian monthly gas production | |
| Arima | Fit ARIMA model to univariate time series | |
| forecast.StructTS | Forecasting using Structural Time Series models | |
| logLik.ets | Log-Likelihood of an ets object | |
| seasadj | Seasonal adjustment | |
| sindexf | Forecast seasonal index | |
| tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| monthdays | Number of days in each season | |
| bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| auto.arima | Fit best ARIMA model to univariate time series | |
| ma | Moving-average smoothing | |
| gold | Daily morning gold prices | |
| na.interp | Interpolate missing values in a time series | |
| arfima | Fit a fractionally differenced ARFIMA model | |
| forecast | Forecasting time series | |
| croston | Forecasts for intermittent demand using Croston's method | |
| seasonplot | Seasonal plot | |
| CV | Cross-validation statistic | |
| fitted.Arima | One-step in-sample forecasts using ARIMA models | |
| plot.forecast | Forecast plot | |
| rwf | Random Walk Forecast | |
| splinef | Cubic Spline Forecast | |
| dshw | Double-Seasonal Holt-Winters Forecasting | |
| forecast.HoltWinters | Forecasting using Holt-Winters objects | |
| BoxCox | Box Cox Transformation | |
| ets | Exponential smoothing state space model | |
| msts | Multi-Seasonal Time Series | |
| forecast.bats | Forecasting using BATS and TBATS models | |
| tsdisplay | Time series display | |
| thetaf | Theta method forecast | |
| forecast.stl | Forecasting using stl objects | |
| naive | Naive forecasts | |
| dm.test | Diebold-Mariano test for predictive accuracy | |
| plot.ets | Plot components from ETS model | |
| simulate.ets | Simulation from a time series model | |
| Acf | (Partial) Autocorrelation Function Estimation | |
| ses | Exponential smoothing forecasts | |
| arima.errors | ARIMA errors | |
| forecast.Arima | Forecasting using ARIMA or ARFIMA models | |
| forecast.ets | Forecasting using ETS models | |
| accuracy | Accuracy measures for forecast model | |
| subset.ts | Subsetting a time series | |
| taylor | Half-hourly electricity demand | |
| meanf | Mean Forecast | |
| seasonaldummy | Seasonal dummy variables | |
| wineind | Australian total wine sales | |
| ndiffs | Number of differences required for a stationary series | |
| forecast.lm | Forecast a linear model with possible time series components | |
| woolyrnq | Quarterly production of woollen yarn in Australia | |
| tslm | Fit a linear model with time series components | |
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Details
| Date | 2012-02-17 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | yes |
| ByteCompile | TRUE |
| License | GPL (>= 2) |
| URL | http://robjhyndman.com/software/forecast/ |
| Packaged | 2012-02-17 03:02:30 UTC; hyndman |
| Repository | CRAN |
| Date/Publication | 2012-02-17 07:38:40 |
| depends | base (>= 2.14.0) , fracdiff , graphics , parallel , R (>= 2.14.0) , Rcpp (>= 0.9.7) , RcppArmadillo (>= 0.2.29) , stats , tseries , zoo |
| Contributors | Rob Hyndman |
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