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forecast (version 3.24)

Forecasting functions for time series and linear models

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

142,544

Version

3.24

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

July 23rd, 2012

Functions in forecast (3.24)

Random Walk Forecast

Number of differences required for a stationary series

One-step in-sample forecasts using ARIMA models

BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Forecasting using ETS models

TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)

Forecasting time series

Naive forecasts

Interpolate missing values in a time series

Number of days in each season

forecast.HoltWinters

Forecasting using Holt-Winters objects

Subsetting a time series

Time series display

Exponential smoothing forecasts

forecast.StructTS

Forecasting using Structural Time Series models

Half-hourly electricity demand

Cross-validation statistic

Forecasting using ARIMA or ARFIMA models

Multi-Seasonal Time Series

Double-Seasonal Holt-Winters Forecasting

Diebold-Mariano test for predictive accuracy

Fit ARIMA model to univariate time series

Log-Likelihood of an ets object

Automatic selection of Box Cox transformation parameter

Plot components from ETS model

Fit a linear model with time series components

Forecasting using stl objects

Simulation from a time series model

Forecast seasonal index

Theta method forecast

Moving-average smoothing

Forecasts for intermittent demand using Croston's method

Daily morning gold prices

Box Cox Transformation

Fit a fractionally differenced ARFIMA model

Quarterly production of woollen yarn in Australia

Cubic Spline Forecast

Australian monthly gas production

Forecasting using BATS and TBATS models

Fit best ARIMA model to univariate time series

Plot components from BATS model

Accuracy measures for forecast model

Exponential smoothing state space model

(Partial) Autocorrelation Function Estimation

Forecast a linear model with possible time series components

Seasonal adjustment

Australian total wine sales

Seasonal dummy variables