Forecasting Functions for Time Series and Linear Models

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 |

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 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.