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forecast (version 4.04)

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

137,850

Version

4.04

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

April 22nd, 2013

Functions in forecast (4.04)

One-step in-sample forecasts using ARIMA models

Fit best ARIMA model to univariate time series

Neural Network Time Series Forecasts

Forecast seasonal index

Forecasting using BATS and TBATS models

Multi-Seasonal Time Series

Exponential smoothing forecasts

Time series display

Moving-average smoothing

Forecast a linear model with possible time series components

Log-Likelihood of an ets object

Forecasting using stl objects

Cross-validation statistic

Double-Seasonal Holt-Winters Forecasting

Forecasts for intermittent demand using Croston's method

forecast.StructTS

Forecasting using Structural Time Series models

forecast.HoltWinters

Forecasting using Holt-Winters objects

Half-hourly electricity demand

Number of differences required for a stationary series

Simulation from a time series model

Forecasting using ETS models

Number of days in each season

tbats.components

Extract components of a TBATS model

Naive forecasts

Australian monthly gas production

Seasonal adjustment

Diebold-Mariano test for predictive accuracy

Interpolate missing values in a time series

Random Walk Forecast

Cubic Spline Forecast

Daily morning gold prices

Accuracy measures for forecast model

Plot components from BATS model

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

Australian total wine sales

Forecasting using ARIMA or ARFIMA models

Fit ARIMA model to univariate time series

Plot components from ETS model

Subsetting a time series

Box Cox Transformation

Exponential smoothing state space model

Fit a linear model with time series components

Quarterly production of woollen yarn in Australia

Automatic selection of Box Cox transformation parameter

Fit a fractionally differenced ARFIMA model

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

Get response variable from time series model.

Forecasting time series

(Partial) Autocorrelation Function Estimation

Theta method forecast

Seasonal dummy variables