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forecast (version 2.11)

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.

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Version

Install

install.packages('forecast')

Monthly Downloads

137,850

Version

2.11

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

November 4th, 2010

Functions in forecast (2.11)

One-step in-sample forecasts using ARIMA models

forecast.StructTS

Forecasting using Structural Time Series models

Fit ARIMA model to univariate time series

forecast.HoltWinters

Forecasting using Holt-Winters objects

Number of differences

Exponential smoothing state space model

Diebold-Mariano test for predictive accuracy

Daily morning gold prices

Accuracy measures for forecast model

Cubic Spline Forecast

Exponential smoothing forecasts

Box Cox Transformation

Random Walk Forecast

Forecasting using ETS models

Forecasting using ARIMA or ARFIMA models

Fit best ARIMA model to univariate time series

Quarterly production of woollen yarn in Australia

Forecasting time series

Seasonal dummy variables

Interpolate missing values in a time series

Log-Likelihood of an ets object

Number of days in each season

Seasonal adjustment

Australian total wine sales

Theta method forecast

Australian monthly gas production

Fit a fractionally differenced ARFIMA model

Forecast seasonal index

Plot components from ETS model

Forecasts for intermittent demand using Croston's method

Time series display

Simulation from a time series model