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forecast (version 2.17)

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

2.17

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

April 6th, 2011

Functions in forecast (2.17)

Fit ARIMA model to univariate time series

Forecasting using stl objects

Australian monthly gas production

Accuracy measures for forecast model

Exponential smoothing forecasts

forecast.HoltWinters

Forecasting using Holt-Winters objects

Classical Seasonal Decomposition by Moving Averages

Random Walk Forecast

Seasonal dummy variables

Fit a fractionally differenced ARFIMA model

Box Cox Transformation

Daily morning gold prices

Diebold-Mariano test for predictive accuracy

One-step in-sample forecasts using ARIMA models

Forecast a linear model with possible time series components

Number of differences

Forecasting using ETS models

Plot components from ETS model

Cubic Spline Forecast

Forecasts for intermittent demand using Croston's method

Log-Likelihood of an ets object

Forecasting using ARIMA or ARFIMA models

Naive forecasts

Time series display

Seasonal adjustment

Australian total wine sales

Forecast seasonal index

Quarterly production of woollen yarn in Australia

Number of days in each season

Fit a linear model with time series components

Exponential smoothing state space model

Interpolate missing values in a time series

Fit best ARIMA model to univariate time series

forecast.StructTS

Forecasting using Structural Time Series models

Theta method forecast

Simulation from a time series model

Forecasting time series