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forecast (version 3.16)

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

110,799

Version

3.16

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

December 24th, 2011

Functions in forecast (3.16)

Number of differences required for a stationary series

Exponential smoothing forecasts

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

Forecasting using stl objects

Diebold-Mariano test for predictive accuracy

Fit ARIMA model to univariate time series

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

Cubic Spline Forecast

Subsetting a time series

forecast.HoltWinters

Forecasting using Holt-Winters objects

Forecasting using ETS models

Forecast a linear model with possible time series components

Naive forecasts

Multi-Seasonal Time Series

Seasonal dummy variables

Fit a fractionally differenced ARFIMA model

Double-Seasonal Holt-Winters Forecasting

Number of days in each season

Interpolate missing values in a time series

Random Walk Forecast

Half-hourly electricity demand

Accuracy measures for forecast model

forecast.StructTS

Forecasting using Structural Time Series models

Simulation from a time series model

One-step in-sample forecasts using ARIMA models

Cross-validation statistic

Moving-average smoothing

Australian monthly gas production

Forecasting time series

Daily morning gold prices

Forecasting using BATS and TBATS models

Fit best ARIMA model to univariate time series

Forecast seasonal index

Exponential smoothing state space model

Australian total wine sales

Fit a linear model with time series components

Time series display

(Partial) Autocorrelation Function Estimation

Box Cox Transformation

Log-Likelihood of an ets object

Seasonal adjustment

Theta method forecast

Automatic selection of Box Cox transformation parameter

Quarterly production of woollen yarn in Australia

Forecasts for intermittent demand using Croston's method

Forecasting using ARIMA or ARFIMA models

Plot components from ETS model