| accuracy.default | Accuracy measures for a forecast model |
| Acf | (Partial) Autocorrelation and Cross-Correlation Function... |
| arfima | Fit a fractionally differenced ARFIMA model |
| Arima | Fit ARIMA model to univariate time series |
| arima.errors | Errors from a regression model with ARIMA errors |
| arimaorder | Return the order of an ARIMA or ARFIMA model |
| auto.arima | Fit best ARIMA model to univariate time series |
| autolayer | Create a ggplot layer appropriate to a particular data type |
| autoplot.acf | ggplot (Partial) Autocorrelation and Cross-Correlation... |
| autoplot.seas | Plot time series decomposition components using ggplot |
| autoplot.ts | Automatically create a ggplot for time series objects |
| baggedModel | Forecasting using a bagged model |
| bats | BATS model (Exponential smoothing state space model with... |
| bizdays | Number of trading days in each season |
| bld.mbb.bootstrap | Box-Cox and Loess-based decomposition bootstrap. |
| BoxCox | Box Cox Transformation |
| BoxCox.lambda | Automatic selection of Box Cox transformation parameter |
| checkresiduals | Check that residuals from a time series model look like white... |
| croston | Forecasts for intermittent demand using Croston's method |
| CV | Cross-validation statistic |
| CVar | k-fold Cross-Validation applied to an autoregressive model |
| dm.test | Diebold-Mariano test for predictive accuracy |
| dshw | Double-Seasonal Holt-Winters Forecasting |
| easter | Easter holidays in each season |
| ets | Exponential smoothing state space model |
| findfrequency | Find dominant frequency of a time series |
| fitted.Arima | h-step in-sample forecasts for time series models. |
| forecast.Arima | Forecasting using ARIMA or ARFIMA models |
| forecast.baggedModel | Forecasting using a bagged model |
| forecast.bats | Forecasting using BATS and TBATS models |
| forecast.ets | Forecasting using ETS models |
| forecast.HoltWinters | Forecasting using Holt-Winters objects |
| forecast.lm | Forecast a linear model with possible time series components |
| forecast.mlm | Forecast a multiple linear model with possible time series... |
| forecast.modelAR | Forecasting using user-defined model |
| forecast.mts | Forecasting time series |
| forecast.nnetar | Forecasting using neural network models |
| forecast-package | forecast: Forecasting Functions for Time Series and Linear... |
| forecast.stl | Forecasting using stl objects |
| forecast.StructTS | Forecasting using Structural Time Series models |
| forecast.ts | Forecasting time series |
| fourier | Fourier terms for modelling seasonality |
| gas | Australian monthly gas production |
| geom_forecast | Forecast plot |
| getResponse | Get response variable from time series model. |
| gghistogram | Histogram with optional normal and kernel density functions |
| gglagplot | Time series lag ggplots |
| ggmonthplot | Create a seasonal subseries ggplot |
| gold | Daily morning gold prices |
| is.constant | Is an object constant? |
| is.ets | Is an object a particular model type? |
| is.forecast | Is an object a particular forecast type? |
| ma | Moving-average smoothing |
| meanf | Mean Forecast |
| modelAR | Time Series Forecasts with a user-defined model |
| modeldf | Compute model degrees of freedom |
| monthdays | Number of days in each season |
| mstl | Multiple seasonal decomposition |
| msts | Multi-Seasonal Time Series |
| na.interp | Interpolate missing values in a time series |
| naive | Naive and Random Walk Forecasts |
| ndiffs | Number of differences required for a stationary series |
| nnetar | Neural Network Time Series Forecasts |
| nsdiffs | Number of differences required for a seasonally stationary... |
| ocsb.test | Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit... |
| plot.Arima | Plot characteristic roots from ARIMA model |
| plot.bats | Plot components from BATS model |
| plot.ets | Plot components from ETS model |
| plot.forecast | Forecast plot |
| plot.mforecast | Multivariate forecast plot |
| reexports | Objects exported from other packages |
| residuals.forecast | Residuals for various time series models |
| seasadj | Seasonal adjustment |
| seasonal | Extract components from a time series decomposition |
| seasonaldummy | Seasonal dummy variables |
| seasonplot | Seasonal plot |
| ses | Exponential smoothing forecasts |
| simulate.ets | Simulation from a time series model |
| sindexf | Forecast seasonal index |
| splinef | Cubic Spline Forecast |
| subset.ts | Subsetting a time series |
| taylor | Half-hourly electricity demand |
| tbats | TBATS model (Exponential smoothing state space model with... |
| tbats.components | Extract components of a TBATS model |
| thetaf | Theta method forecast |
| tsclean | Identify and replace outliers and missing values in a time... |
| tsCV | Time series cross-validation |
| tsdisplay | Time series display |
| tslm | Fit a linear model with time series components |
| tsoutliers | Identify and replace outliers in a time series |
| wineind | Australian total wine sales |
| woolyrnq | Quarterly production of woollen yarn in Australia |
