forecast v3.24
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 | |
| rwf | Random Walk Forecast | |
| ndiffs | Number of differences required for a stationary series | |
| fitted.Arima | One-step in-sample forecasts using ARIMA models | |
| bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| forecast.ets | Forecasting using ETS models | |
| tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| forecast | Forecasting time series | |
| plot.forecast | Forecast plot | |
| naive | Naive forecasts | |
| na.interp | Interpolate missing values in a time series | |
| monthdays | Number of days in each season | |
| forecast.HoltWinters | Forecasting using Holt-Winters objects | |
| subset.ts | Subsetting a time series | |
| seasonplot | Seasonal plot | |
| tsdisplay | Time series display | |
| ses | Exponential smoothing forecasts | |
| forecast.StructTS | Forecasting using Structural Time Series models | |
| taylor | Half-hourly electricity demand | |
| CV | Cross-validation statistic | |
| forecast.Arima | Forecasting using ARIMA or ARFIMA models | |
| msts | Multi-Seasonal Time Series | |
| dshw | Double-Seasonal Holt-Winters Forecasting | |
| dm.test | Diebold-Mariano test for predictive accuracy | |
| Arima | Fit ARIMA model to univariate time series | |
| logLik.ets | Log-Likelihood of an ets object | |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
| plot.ets | Plot components from ETS model | |
| tslm | Fit a linear model with time series components | |
| forecast.stl | Forecasting using stl objects | |
| simulate.ets | Simulation from a time series model | |
| sindexf | Forecast seasonal index | |
| thetaf | Theta method forecast | |
| ma | Moving-average smoothing | |
| croston | Forecasts for intermittent demand using Croston's method | |
| gold | Daily morning gold prices | |
| BoxCox | Box Cox Transformation | |
| arfima | Fit a fractionally differenced ARFIMA model | |
| woolyrnq | Quarterly production of woollen yarn in Australia | |
| splinef | Cubic Spline Forecast | |
| gas | Australian monthly gas production | |
| forecast.bats | Forecasting using BATS and TBATS models | |
| arima.errors | ARIMA errors | |
| auto.arima | Fit best ARIMA model to univariate time series | |
| plot.bats | Plot components from BATS model | |
| accuracy | Accuracy measures for forecast model | |
| ets | Exponential smoothing state space model | |
| Acf | (Partial) Autocorrelation Function Estimation | |
| forecast.lm | Forecast a linear model with possible time series components | |
| seasadj | Seasonal adjustment | |
| wineind | Australian total wine sales | |
| meanf | Mean Forecast | |
| seasonaldummy | Seasonal dummy variables | |
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Details
| Date | 2012-07-23 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | yes |
| ByteCompile | TRUE |
| License | GPL (>= 2) |
| URL | http://robjhyndman.com/software/forecast/ |
| Packaged | 2012-07-23 04:54:49 UTC; hyndman |
| Repository | CRAN |
| Date/Publication | 2012-07-23 05:26:03 |
| depends | base (>= 2.14.0) , fracdiff , graphics , parallel , R (>= 2.14.0) , Rcpp (>= 0.9.10) , RcppArmadillo (>= 0.2.35) , stats , tseries , zoo |
| Contributors | Rob Hyndman |
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