Learn R Programming

⚠️There's a newer version (8.24.0) of this package.Take me there.

forecast (version 2.07)

Forecasting functions for time series

Description

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Copy Link

Version

Install

install.packages('forecast')

Monthly Downloads

191,858

Version

2.07

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

September 9th, 2010

Functions in forecast (2.07)

Box Cox Transformation

Fit a fractionally differenced ARFIMA model

Forecasting using ETS models

Forecasting time series

Exponential smoothing state space model

Diebold-Mariano test for predictive accuracy

Simulation from a time series model

Daily morning gold prices

Plot components from ETS model

Australian total wine sales

Exponential smoothing forecasts

Accuracy measures for forecast model

Number of days in each season

Number of differences

Fit ARIMA model to univariate time series

Theta method forecast

Seasonal dummy variables

Random Walk Forecast

Log-Likelihood of an ets object

Interpolate missing values in a time series

Forecasting using ARIMA or ARFIMA models

forecast.StructTS

Forecasting using Structural Time Series models

Quarterly production of woollen yarn in Australia

Forecasts for intermittent demand using Croston's method

Time series display

forecast.HoltWinters

Forecasting using Holt-Winters objects

Seasonal adjustment

Cubic Spline Forecast

Australian monthly gas production

Forecast seasonal index

Fit best ARIMA model to univariate time series

One-step in-sample forecasts using ARIMA models