Learn R Programming

⚠️There's a newer version (9.0.0) of this package.Take me there.

forecast (version 3.17)

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

428,345

Version

3.17

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

February 2nd, 2012

Functions in forecast (3.17)

Number of days in each season

Number of differences required for a stationary series

Box Cox Transformation

Accuracy measures for forecast model

Australian total wine sales

Exponential smoothing state space model

Double-Seasonal Holt-Winters Forecasting

Diebold-Mariano test for predictive accuracy

Forecasting using ETS models

(Partial) Autocorrelation Function Estimation

Seasonal adjustment

Australian monthly gas production

Moving-average smoothing

Fit ARIMA model to univariate time series

Forecast a linear model with possible time series components

Fit a linear model with time series components

Interpolate missing values in a time series

Naive forecasts

Forecasts for intermittent demand using Croston's method

Log-Likelihood of an ets object

Cross-validation statistic

Theta method forecast

Fit a fractionally differenced ARFIMA model

Random Walk Forecast

Forecasting using BATS and TBATS models

forecast.HoltWinters

Forecasting using Holt-Winters objects

BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Half-hourly electricity demand

Plot components from ETS model

Multi-Seasonal Time Series

Automatic selection of Box Cox transformation parameter

TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Fit best ARIMA model to univariate time series

Quarterly production of woollen yarn in Australia

forecast.StructTS

Forecasting using Structural Time Series models

Time series display

Exponential smoothing forecasts

Subsetting a time series

One-step in-sample forecasts using ARIMA models

Forecasting time series

Simulation from a time series model

Forecasting using ARIMA or ARFIMA models

Seasonal dummy variables

Forecasting using stl objects

Cubic Spline Forecast

Daily morning gold prices

Forecast seasonal index