forecast v4.03
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 | |
| forecast.Arima | Forecasting using ARIMA or ARFIMA models | |
| sindexf | Forecast seasonal index | |
| thetaf | Theta method forecast | |
| tslm | Fit a linear model with time series components | |
| wineind | Australian total wine sales | |
| dshw | Double-Seasonal Holt-Winters Forecasting | |
| ndiffs | Number of differences required for a stationary series | |
| Arima | Fit ARIMA model to univariate time series | |
| ses | Exponential smoothing forecasts | |
| plot.forecast | Forecast plot | |
| tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| BoxCox | Box Cox Transformation | |
| Acf | (Partial) Autocorrelation Function Estimation | |
| na.interp | Interpolate missing values in a time series | |
| simulate.ets | Simulation from a time series model | |
| monthdays | Number of days in each season | |
| seasonplot | Seasonal plot | |
| CV | Cross-validation statistic | |
| croston | Forecasts for intermittent demand using Croston's method | |
| arima.errors | ARIMA errors | |
| forecast.stl | Forecasting using stl objects | |
| fitted.Arima | One-step in-sample forecasts using ARIMA models | |
| seasadj | Seasonal adjustment | |
| forecast.HoltWinters | Forecasting using Holt-Winters objects | |
| seasonaldummy | Seasonal dummy variables | |
| tsdisplay | Time series display | |
| tbats.components | Extract components of a TBATS model | |
| auto.arima | Fit best ARIMA model to univariate time series | |
| accuracy | Accuracy measures for forecast model | |
| splinef | Cubic Spline Forecast | |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
| bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| dm.test | Diebold-Mariano test for predictive accuracy | |
| taylor | Half-hourly electricity demand | |
| arfima | Fit a fractionally differenced ARFIMA model | |
| forecast | Forecasting time series | |
| logLik.ets | Log-Likelihood of an ets object | |
| ma | Moving-average smoothing | |
| meanf | Mean Forecast | |
| woolyrnq | Quarterly production of woollen yarn in Australia | |
| forecast.lm | Forecast a linear model with possible time series components | |
| msts | Multi-Seasonal Time Series | |
| forecast.bats | Forecasting using BATS and TBATS models | |
| forecast.StructTS | Forecasting using Structural Time Series models | |
| naive | Naive forecasts | |
| plot.ets | Plot components from ETS model | |
| forecast.ets | Forecasting using ETS models | |
| getResponse | Get response variable from time series model. | |
| plot.bats | Plot components from BATS model | |
| nnetar | Neural Network Time Series Forecasts | |
| subset.ts | Subsetting a time series | |
| rwf | Random Walk Forecast | |
| gas | Australian monthly gas production | |
| gold | Daily morning gold prices | |
| ets | Exponential smoothing state space model | |
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Details
| Date | 2013-03-14 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | yes |
| ByteCompile | TRUE |
| License | GPL (>= 2) |
| URL | http://robjhyndman.com/software/forecast/ |
| Packaged | 2013-03-17 11:01:14 UTC; hyndman |
| NeedsCompilation | yes |
| Repository | CRAN |
| Date/Publication | 2013-03-17 12:28:31 |
| depends | base (>= 2.14.0) , graphics , R (>= 2.14.0) , stats |
| imports | colorspace , fracdiff , nnet , parallel , Rcpp (>= 0.9.10) , RcppArmadillo (>= 0.2.35) , tseries , zoo |
| Contributors | Rob Hyndman |
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