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forecast (version 2.12)

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

213,748

Version

2.12

License

GPL (>= 2)

Maintainer

Last Published

January 19th, 2011

Functions in forecast (2.12)

Forecasts for intermittent demand using Croston's method

Cubic Spline Forecast

Fit ARIMA model to univariate time series

Time series display

Plot components from ETS model

Forecasting time series

Quarterly production of woollen yarn in Australia

Forecasting using ETS models

Diebold-Mariano test for predictive accuracy

Accuracy measures for forecast model

Fit best ARIMA model to univariate time series

Australian total wine sales

Number of days in each season

Forecast seasonal index

Australian monthly gas production

Number of differences

Fit a fractionally differenced ARFIMA model

Simulation from a time series model

Seasonal dummy variables

Box Cox Transformation

Naive forecasts

Interpolate missing values in a time series

Log-Likelihood of an ets object

forecast.HoltWinters

Forecasting using Holt-Winters objects

Random Walk Forecast

Seasonal adjustment

Exponential smoothing state space model

One-step in-sample forecasts using ARIMA models

Forecasting using ARIMA or ARFIMA models

forecast.StructTS

Forecasting using Structural Time Series models

Exponential smoothing forecasts

Theta method forecast

Daily morning gold prices