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forecast (version 3.22)

Forecasting functions for time series and linear models

Description

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

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Version

Install

install.packages('forecast')

Monthly Downloads

182,360

Version

3.22

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

June 7th, 2012

Functions in forecast (3.22)

Australian monthly gas production

Half-hourly electricity demand

Fit a fractionally differenced ARFIMA model

Seasonal adjustment

Multi-Seasonal Time Series

Australian total wine sales

Simulation from a time series model

Accuracy measures for forecast model

Daily morning gold prices

Automatic selection of Box Cox transformation parameter

Double-Seasonal Holt-Winters Forecasting

Plot components from ETS model

Cross-validation statistic

Quarterly production of woollen yarn in Australia

Fit ARIMA model to univariate time series

Subsetting a time series

Forecasting using BATS and TBATS models

Box Cox Transformation

Naive forecasts

Number of differences required for a stationary series

Cubic Spline Forecast

Forecasting using stl objects

Diebold-Mariano test for predictive accuracy

Fit best ARIMA model to univariate time series

Log-Likelihood of an ets object

forecast.HoltWinters

Forecasting using Holt-Winters objects

Forecasting using ARIMA or ARFIMA models

Interpolate missing values in a time series

One-step in-sample forecasts using ARIMA models

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

forecast.StructTS

Forecasting using Structural Time Series models

Moving-average smoothing

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

(Partial) Autocorrelation Function Estimation

Random Walk Forecast

Forecasting using ETS models

Number of days in each season

Theta method forecast

Fit a linear model with time series components

Time series display

Forecast a linear model with possible time series components

Forecasting time series

Seasonal dummy variables

Exponential smoothing forecasts

Forecast seasonal index

Plot components from BATS model

Exponential smoothing state space model

Forecasts for intermittent demand using Croston's method