| Accuracy measures for a forecast model | accuracy.Arima accuracy.fc_model accuracy.forecast accuracy.lm accuracy.mforecast accuracy.numeric accuracy.ts |
| (Partial) Autocorrelation and Cross-Correlation Function Estimation | Acf Ccf Pacf taperedacf taperedpacf |
| Fit a fractionally differenced ARFIMA model | arfima |
| Fit ARIMA model to univariate time series | Arima as.character.Arima print.ARIMA summary.Arima |
| Errors from a regression model with ARIMA errors | arima.errors |
| Return the order of an ARIMA or ARFIMA model | arimaorder |
| Fit best ARIMA model to univariate time series | auto.arima |
| Automatically create a ggplot for time series objects | autolayer.msts autolayer.mts autolayer.ts autoplot.msts autoplot.mts autoplot.ts fortify.ts |
| ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting | autoplot.acf autoplot.mpacf ggAcf ggCcf ggPacf ggtaperedacf ggtaperedpacf |
| Plot time series decomposition components using ggplot | autoplot.decomposed.ts autoplot.mstl autoplot.seas autoplot.stl autoplot.StructTS |
| Multivariate forecast plot | autolayer.mforecast autoplot.mforecast plot.mforecast |
| Forecasting using a bagged model | baggedETS baggedModel print.baggedModel |
| BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | as.character.bats bats print.bats |
| Number of trading days in each season | bizdays |
| Box-Cox and Loess-based decomposition bootstrap. | bld.mbb.bootstrap |
| Box Cox Transformation | BoxCox InvBoxCox |
| Automatic selection of Box Cox transformation parameter | BoxCox.lambda |
| Check that residuals from a time series model look like white noise | checkresiduals |
| Croston forecast model | croston_model |
| Cross-validation statistic | CV |
| k-fold Cross-Validation applied to an autoregressive model | CVar print.CVar |
| Diebold-Mariano test for predictive accuracy | dm.test |
| Double-Seasonal Holt-Winters Forecasting | dshw |
| Easter holidays in each season | easter |
| Exponential smoothing state space model | as.character.ets coef.ets ets print.ets summary.ets tsdiag.ets |
| Find dominant frequency of a time series | findfrequency |
| h-step in-sample forecasts for time series models. | fitted.ar fitted.ARFIMA fitted.Arima fitted.bats fitted.ets fitted.forecast_ARIMA fitted.modelAR fitted.nnetar fitted.tbats |
| Forecasting using a bagged model | forecast.baggedModel |
| Forecasting using BATS and TBATS models | forecast.bats forecast.tbats |
| Forecasts for intermittent demand using Croston's method | croston forecast.croston_model |
| Forecasting using ETS models | forecast.ets |
| Forecasting using ARIMA or ARFIMA models | forecast.ar forecast.Arima forecast.forecast_ARIMA forecast.fracdiff |
| Forecasting using Holt-Winters objects | forecast.HoltWinters |
| Forecast a linear model with possible time series components | forecast.lm |
| Mean Forecast | forecast.mean_model meanf |
| Forecast a multiple linear model with possible time series components | forecast.mlm |
| Forecasting using user-defined model | forecast.modelAR |
| Forecasting time series | as.data.frame.mforecast forecast.mts mforecast print.mforecast summary.mforecast |
| Forecasting using neural network models | forecast.nnetar |
| Naive and Random Walk Forecasts | forecast.rw_model naive rwf snaive |
| Returns local linear forecasts and prediction intervals using cubic smoothing splines estimated with 'spline_model()'. | forecast.spline_model splinef |
| Forecasting using stl objects | forecast.stl forecast.stlm stlf |
| Forecasting using Structural Time Series models | forecast.StructTS |
| Theta method forecasts. | forecast.theta_model thetaf |
| Forecasting time series | as.data.frame.forecast as.ts.forecast forecast.default forecast.ts print.forecast summary.forecast |
| Fourier terms for modelling seasonality | fourier fourierf |
| Australian monthly gas production | gas |
| Get response variable from time series model. | getResponse getResponse.ar getResponse.Arima getResponse.baggedModel getResponse.bats getResponse.default getResponse.fracdiff getResponse.lm getResponse.mforecast getResponse.tbats |
| Histogram with optional normal and kernel density functions | gghistogram |
| Time series lag ggplots | gglagchull gglagplot |
| Create a seasonal subseries ggplot | ggmonthplot ggsubseriesplot |
| Seasonal plot | ggseasonplot seasonplot |
| Time series display | ggtsdisplay tsdisplay |
| Daily morning gold prices | gold |
| Is an object a particular model type? | is.acf is.Arima is.baggedModel is.bats is.ets is.modelAR is.nnetar is.nnetarmodels is.stlm |
| Is an object constant? | is.constant |
| Is an object a particular forecast type? | is.forecast is.mforecast is.splineforecast |
| Moving-average smoothing | ma |
| Mean Forecast Model | mean_model |
| Time Series Forecasts with a user-defined model | modelAR print.modelAR |
| Compute model degrees of freedom | modeldf |
| Number of days in each season | monthdays |
| Multiple seasonal decomposition | mstl |
| Multi-Seasonal Time Series | msts print.msts window.msts `[.msts` |
| Interpolate missing values in a time series | na.interp |
| Number of differences required for a stationary series | ndiffs |
| Neural Network Time Series Forecasts | nnetar print.nnetar print.nnetarmodels |
| Number of differences required for a seasonally stationary series | nsdiffs |
| Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots | ocsb.test print.OCSBtest |
| Plot characteristic roots from ARIMA model | autoplot.ar autoplot.Arima plot.ar plot.Arima |
| Plot components from BATS model | autoplot.bats autoplot.tbats plot.bats plot.tbats |
| Plot components from ETS model | autoplot.ets plot.ets |
| Forecast plot | autolayer.forecast autoplot.forecast autoplot.splineforecast plot.forecast plot.splineforecast |
| Residuals for various time series models | residuals.ar residuals.ARFIMA residuals.Arima residuals.bats residuals.ets residuals.forecast residuals.forecast_ARIMA residuals.nnetar residuals.stlm residuals.tbats residuals.tslm |
| Random walk model | rw_model |
| Seasonal adjustment | seasadj seasadj.decomposed.ts seasadj.mstl seasadj.seas seasadj.stl seasadj.tbats |
| Extract components from a time series decomposition | remainder seasonal trendcycle |
| Seasonal dummy variables | seasonaldummy seasonaldummyf |
| Exponential smoothing forecasts | holt hw ses |
| Simulation from a time series model | simulate.ar simulate.Arima simulate.ets simulate.fracdiff simulate.modelAR simulate.nnetar simulate.rw_model simulate.spline_model simulate.tbats |
| Forecast seasonal index | sindexf |
| Cubic spline stochastic model | spline_model |
| Forecast plot | GeomForecast geom_forecast StatForecast |
| Forecasting model using STL with a generative time series model | stlm |
| Subsetting a time series | subset.msts subset.ts |
| Half-hourly electricity demand | taylor |
| TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | as.character.tbats print.tbats tbats |
| Extract components of a TBATS model | tbats.components |
| Theta model | theta_model |
| Identify and replace outliers and missing values in a time series | tsclean |
| Time series cross-validation | tsCV |
| Fit a linear model with time series components | tslm |
| Identify and replace outliers in a time series | tsoutliers |
| Australian total wine sales | wineind |
| Quarterly production of woollen yarn in Australia | woolyrnq |
