forecast v4.8
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 | |
| 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) | |
| Arima | Fit ARIMA model to univariate time series | |
| taylor | Half-hourly electricity demand | |
| ets | Exponential smoothing state space model | |
| meanf | Mean Forecast | |
| plot.ets | Plot components from ETS model | |
| dshw | Double-Seasonal Holt-Winters Forecasting | |
| auto.arima | Fit best ARIMA model to univariate time series | |
| gold | Daily morning gold prices | |
| arima.errors | ARIMA errors | |
| forecast.HoltWinters | Forecasting using Holt-Winters objects | |
| monthdays | Number of days in each season | |
| seasadj | Seasonal adjustment | |
| seasonplot | Seasonal plot | |
| forecast.ets | Forecasting using ETS models | |
| na.interp | Interpolate missing values in a time series | |
| tbats.components | Extract components of a TBATS model | |
| msts | Multi-Seasonal Time Series | |
| forecast.bats | Forecasting using BATS and TBATS models | |
| forecast.stl | Forecasting using stl objects | |
| simulate.ets | Simulation from a time series model | |
| BoxCox | Box Cox Transformation | |
| dm.test | Diebold-Mariano test for predictive accuracy | |
| fitted.Arima | One-step in-sample forecasts using ARIMA models | |
| plot.forecast | Forecast plot | |
| forecast.lm | Forecast a linear model with possible time series components | |
| ses | Exponential smoothing forecasts | |
| subset.ts | Subsetting a time series | |
| forecast | Forecasting time series | |
| Acf | (Partial) Autocorrelation Function Estimation | |
| croston | Forecasts for intermittent demand using Croston's method | |
| plot.bats | Plot components from BATS model | |
| gas | Australian monthly gas production | |
| wineind | Australian total wine sales | |
| nnetar | Neural Network Time Series Forecasts | |
| tsdisplay | Time series display | |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
| ndiffs | Number of differences required for a stationary series | |
| logLik.ets | Log-Likelihood of an ets object | |
| naive | Naive forecasts | |
| bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| CV | Cross-validation statistic | |
| splinef | Cubic Spline Forecast | |
| woolyrnq | Quarterly production of woollen yarn in Australia | |
| sindexf | Forecast seasonal index | |
| rwf | Random Walk Forecast | |
| getResponse | Get response variable from time series model. | |
| ma | Moving-average smoothing | |
| seasonaldummy | Seasonal dummy variables | |
| arfima | Fit a fractionally differenced ARFIMA model | |
| thetaf | Theta method forecast | |
| accuracy | Accuracy measures for forecast model | |
| tslm | Fit a linear model with time series components | |
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Details
| Date | 2013-09-30 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | yes |
| ByteCompile | TRUE |
| License | GPL (>= 2) |
| URL | http://robjhyndman.com/software/forecast/ |
| Packaged | 2013-09-30 00:14:10 UTC; hyndman |
| NeedsCompilation | yes |
| Repository | CRAN |
| Date/Publication | 2013-09-30 08:07:30 |
| 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 |
| suggests | Rmalschains |
| Contributors | Rob Hyndman |
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