forecast v4.7
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 | |
| Arima | Fit ARIMA model to univariate time series | |
| getResponse | Get response variable from time series model. | |
| forecast.StructTS | Forecasting using Structural Time Series models | |
| seasadj | Seasonal adjustment | |
| forecast.HoltWinters | Forecasting using Holt-Winters objects | |
| logLik.ets | Log-Likelihood of an ets object | |
| naive | Naive forecasts | |
| rwf | Random Walk Forecast | |
| plot.bats | Plot components from BATS model | |
| accuracy | Accuracy measures for forecast model | |
| seasonaldummy | Seasonal dummy variables | |
| BoxCox | Box Cox Transformation | |
| ndiffs | Number of differences required for a stationary series | |
| splinef | Cubic Spline Forecast | |
| ses | Exponential smoothing forecasts | |
| subset.ts | Subsetting a time series | |
| simulate.ets | Simulation from a time series model | |
| nnetar | Neural Network Time Series Forecasts | |
| tbats.components | Extract components of a TBATS model | |
| arima.errors | ARIMA errors | |
| dshw | Double-Seasonal Holt-Winters Forecasting | |
| forecast.lm | Forecast a linear model with possible time series components | |
| Acf | (Partial) Autocorrelation Function Estimation | |
| thetaf | Theta method forecast | |
| tsdisplay | Time series display | |
| tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| wineind | Australian total wine sales | |
| forecast.stl | Forecasting using stl objects | |
| auto.arima | Fit best ARIMA model to univariate time series | |
| CV | Cross-validation statistic | |
| arfima | Fit a fractionally differenced ARFIMA model | |
| seasonplot | Seasonal plot | |
| meanf | Mean Forecast | |
| forecast.ets | Forecasting using ETS models | |
| ets | Exponential smoothing state space model | |
| gold | Daily morning gold prices | |
| dm.test | Diebold-Mariano test for predictive accuracy | |
| taylor | Half-hourly electricity demand | |
| monthdays | Number of days in each season | |
| plot.ets | Plot components from ETS model | |
| plot.forecast | Forecast plot | |
| sindexf | Forecast seasonal index | |
| tslm | Fit a linear model with time series components | |
| croston | Forecasts for intermittent demand using Croston's method | |
| msts | Multi-Seasonal Time Series | |
| gas | Australian monthly gas production | |
| na.interp | Interpolate missing values in a time series | |
| woolyrnq | Quarterly production of woollen yarn in Australia | |
| bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
| fitted.Arima | One-step in-sample forecasts using ARIMA models | |
| ma | Moving-average smoothing | |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
| forecast.bats | Forecasting using BATS and TBATS models | |
| forecast.Arima | Forecasting using ARIMA or ARFIMA models | |
| forecast | Forecasting time series | |
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Details
| Date | 2013-09-26 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | yes |
| ByteCompile | TRUE |
| License | GPL (>= 2) |
| URL | http://robjhyndman.com/software/forecast/ |
| Packaged | 2013-09-27 00:02:02 UTC; hyndman |
| NeedsCompilation | yes |
| Repository | CRAN |
| Date/Publication | 2013-09-27 07:54:57 |
| 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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