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forecast (version 2.15)

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

191,858

Version

2.15

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

March 5th, 2011

Functions in forecast (2.15)

Accuracy measures for forecast model

Time series display

Plot components from ETS model

Log-Likelihood of an ets object

Cubic Spline Forecast

Forecasting using stl objects

Box Cox Transformation

Forecasting time series

Seasonal adjustment

Forecast a linear model with possible time series components

Number of differences

forecast.StructTS

Forecasting using Structural Time Series models

Number of days in each season

Daily morning gold prices

Forecasting using ARIMA or ARFIMA models

Fit ARIMA model to univariate time series

Theta method forecast

Fit a linear model with time series components

Interpolate missing values in a time series

Forecasts for intermittent demand using Croston's method

Seasonal dummy variables

Forecasting using ETS models

Fit best ARIMA model to univariate time series

Naive forecasts

forecast.HoltWinters

Forecasting using Holt-Winters objects

Quarterly production of woollen yarn in Australia

Diebold-Mariano test for predictive accuracy

Australian total wine sales

Exponential smoothing forecasts

Fit a fractionally differenced ARFIMA model

Simulation from a time series model

Forecast seasonal index

Random Walk Forecast

One-step in-sample forecasts using ARIMA models

Exponential smoothing state space model

Australian monthly gas production