forecast v4.01
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 | |
| accuracy | Accuracy measures for forecast model | |
| forecast.HoltWinters | Forecasting using Holt-Winters objects | |
| forecast.ets | Forecasting using ETS models | |
| Arima | Fit ARIMA model to univariate time series | |
| plot.ets | Plot components from ETS model | |
| seasonplot | Seasonal plot | |
| Acf | (Partial) Autocorrelation Function Estimation | |
| 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) | |
| seasonaldummy | Seasonal dummy variables | |
| BoxCox | Box Cox Transformation | |
| CV | Cross-validation statistic | |
| getResponse | Get response variable from time series model. | |
| msts | Multi-Seasonal Time Series | |
| dshw | Double-Seasonal Holt-Winters Forecasting | |
| subset.ts | Subsetting a time series | |
| sindexf | Forecast seasonal index | |
| seasadj | Seasonal adjustment | |
| forecast.stl | Forecasting using stl objects | |
| forecast.Arima | Forecasting using ARIMA or ARFIMA models | |
| tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| tsdisplay | Time series display | |
| forecast.lm | Forecast a linear model with possible time series components | |
| simulate.ets | Simulation from a time series model | |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
| tslm | Fit a linear model with time series components | |
| gas | Australian monthly gas production | |
| fitted.Arima | One-step in-sample forecasts using ARIMA models | |
| naive | Naive forecasts | |
| auto.arima | Fit best ARIMA model to univariate time series | |
| plot.bats | Plot components from BATS model | |
| ses | Exponential smoothing forecasts | |
| taylor | Half-hourly electricity demand | |
| croston | Forecasts for intermittent demand using Croston's method | |
| forecast.StructTS | Forecasting using Structural Time Series models | |
| ets | Exponential smoothing state space model | |
| nnetar | Neural Network Time Series Forecasts | |
| rwf | Random Walk Forecast | |
| wineind | Australian total wine sales | |
| splinef | Cubic Spline Forecast | |
| forecast.bats | Forecasting using BATS and TBATS models | |
| ma | Moving-average smoothing | |
| logLik.ets | Log-Likelihood of an ets object | |
| ndiffs | Number of differences required for a stationary series | |
| thetaf | Theta method forecast | |
| plot.forecast | Forecast plot | |
| meanf | Mean Forecast | |
| gold | Daily morning gold prices | |
| na.interp | Interpolate missing values in a time series | |
| arfima | Fit a fractionally differenced ARFIMA model | |
| woolyrnq | Quarterly production of woollen yarn in Australia | |
| arima.errors | ARIMA errors | |
| dm.test | Diebold-Mariano test for predictive accuracy | |
| forecast | Forecasting time series | |
| No Results! | ||
Last month downloads
Details
| Date | 2013-01-22 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | yes |
| ByteCompile | TRUE |
| License | GPL (>= 2) |
| URL | http://robjhyndman.com/software/forecast/ |
| Packaged | 2013-01-22 03:32:35 UTC; hyndman |
| Repository | CRAN |
| Date/Publication | 2013-01-22 08:23:33 |
| 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 |
Include our badge in your README
[](http://www.rdocumentation.org/packages/forecast)