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forecast (version 2.16)

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

205,453

Version

2.16

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

March 7th, 2011

Functions in forecast (2.16)

forecast.HoltWinters

Forecasting using Holt-Winters objects

Simulation from a time series model

Exponential smoothing forecasts

One-step in-sample forecasts using ARIMA models

Quarterly production of woollen yarn in Australia

Forecasts for intermittent demand using Croston's method

Forecasting using stl objects

Naive forecasts

Fit ARIMA model to univariate time series

Box Cox Transformation

Seasonal dummy variables

Number of differences

Forecasting using ETS models

Fit a fractionally differenced ARFIMA model

Fit a linear model with time series components

Seasonal adjustment

Forecasting using ARIMA or ARFIMA models

Log-Likelihood of an ets object

Forecast a linear model with possible time series components

Australian total wine sales

Cubic Spline Forecast

Theta method forecast

Accuracy measures for forecast model

Daily morning gold prices

Fit best ARIMA model to univariate time series

Interpolate missing values in a time series

Diebold-Mariano test for predictive accuracy

Number of days in each season

Exponential smoothing state space model

Forecast seasonal index

Time series display

Plot components from ETS model

Australian monthly gas production

Random Walk Forecast

Forecasting time series

forecast.StructTS

Forecasting using Structural Time Series models