forecast v4.05
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 | |
| getResponse | Get response variable from time series model. | |
| taylor | Half-hourly electricity demand | |
| bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| BoxCox | Box Cox Transformation | |
| naive | Naive forecasts | |
| forecast.StructTS | Forecasting using Structural Time Series models | |
| CV | Cross-validation statistic | |
| tbats.components | Extract components of a TBATS model | |
| seasonplot | Seasonal plot | |
| forecast.Arima | Forecasting using ARIMA or ARFIMA models | |
| arima.errors | ARIMA errors | |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
| thetaf | Theta method forecast | |
| fitted.Arima | One-step in-sample forecasts using ARIMA models | |
| forecast.HoltWinters | Forecasting using Holt-Winters objects | |
| splinef | Cubic Spline Forecast | |
| arfima | Fit a fractionally differenced ARFIMA model | |
| tslm | Fit a linear model with time series components | |
| monthdays | Number of days in each season | |
| forecast.stl | Forecasting using stl objects | |
| gas | Australian monthly gas production | |
| ma | Moving-average smoothing | |
| woolyrnq | Quarterly production of woollen yarn in Australia | |
| dm.test | Diebold-Mariano test for predictive accuracy | |
| Acf | (Partial) Autocorrelation Function Estimation | |
| croston | Forecasts for intermittent demand using Croston's method | |
| forecast.ets | Forecasting using ETS models | |
| accuracy | Accuracy measures for forecast model | |
| meanf | Mean Forecast | |
| tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| plot.ets | Plot components from ETS model | |
| forecast.lm | Forecast a linear model with possible time series components | |
| tsdisplay | Time series display | |
| plot.forecast | Forecast plot | |
| forecast.bats | Forecasting using BATS and TBATS models | |
| plot.bats | Plot components from BATS model | |
| sindexf | Forecast seasonal index | |
| ndiffs | Number of differences required for a stationary series | |
| gold | Daily morning gold prices | |
| wineind | Australian total wine sales | |
| logLik.ets | Log-Likelihood of an ets object | |
| na.interp | Interpolate missing values in a time series | |
| auto.arima | Fit best ARIMA model to univariate time series | |
| Arima | Fit ARIMA model to univariate time series | |
| msts | Multi-Seasonal Time Series | |
| rwf | Random Walk Forecast | |
| seasadj | Seasonal adjustment | |
| ets | Exponential smoothing state space model | |
| dshw | Double-Seasonal Holt-Winters Forecasting | |
| nnetar | Neural Network Time Series Forecasts | |
| forecast | Forecasting time series | |
| seasonaldummy | Seasonal dummy variables | |
| simulate.ets | Simulation from a time series model | |
| ses | Exponential smoothing forecasts | |
| subset.ts | Subsetting a time series | |
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Details
| Date | 2013-06-19 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | yes |
| ByteCompile | TRUE |
| License | GPL (>= 2) |
| URL | http://robjhyndman.com/software/forecast/ |
| Packaged | 2013-06-19 04:21:12 UTC; hyndman |
| NeedsCompilation | yes |
| Repository | CRAN |
| Date/Publication | 2013-06-19 08:53:21 |
| 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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