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forecast (version 2.19)

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

2.19

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

June 4th, 2011

Functions in forecast (2.19)

One-step in-sample forecasts using ARIMA models

forecast.HoltWinters

Forecasting using Holt-Winters objects

Naive forecasts

Fit a fractionally differenced ARFIMA model

Forecasting time series

Accuracy measures for forecast model

Forecasting using stl objects

Exponential smoothing state space model

Australian total wine sales

Seasonal adjustment

Classical Seasonal Decomposition by Moving Averages

Forecasts for intermittent demand using Croston's method

Interpolate missing values in a time series

Forecast seasonal index

Forecasting using ETS models

Theta method forecast

Fit ARIMA model to univariate time series

Log-Likelihood of an ets object

Fit best ARIMA model to univariate time series

Forecast a linear model with possible time series components

forecast.StructTS

Forecasting using Structural Time Series models

Time series display

Number of days in each season

Seasonal dummy variables

Number of differences

Daily morning gold prices

Moving-average smoothing

Quarterly production of woollen yarn in Australia

(Partial) Autocorrelation Function Estimation

Box Cox Transformation

Forecasting using ARIMA or ARFIMA models

Exponential smoothing forecasts

Fit a linear model with time series components

Diebold-Mariano test for predictive accuracy

Simulation from a time series model

Cross-validation statistic

Cubic Spline Forecast

Random Walk Forecast

Plot components from ETS model

Australian monthly gas production