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forecast (version 4.03)

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

4.03

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

March 17th, 2013

Functions in forecast (4.03)

Forecasting using ARIMA or ARFIMA models

Forecast seasonal index

Theta method forecast

Fit a linear model with time series components

Australian total wine sales

Double-Seasonal Holt-Winters Forecasting

Number of differences required for a stationary series

Fit ARIMA model to univariate time series

Exponential smoothing forecasts

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

Box Cox Transformation

(Partial) Autocorrelation Function Estimation

Interpolate missing values in a time series

Simulation from a time series model

Number of days in each season

Cross-validation statistic

Forecasts for intermittent demand using Croston's method

Forecasting using stl objects

One-step in-sample forecasts using ARIMA models

Seasonal adjustment

forecast.HoltWinters

Forecasting using Holt-Winters objects

Seasonal dummy variables

Time series display

tbats.components

Extract components of a TBATS model

Fit best ARIMA model to univariate time series

Accuracy measures for forecast model

Cubic Spline Forecast

Automatic selection of Box Cox transformation parameter

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

Diebold-Mariano test for predictive accuracy

Half-hourly electricity demand

Fit a fractionally differenced ARFIMA model

Forecasting time series

Log-Likelihood of an ets object

Moving-average smoothing

Quarterly production of woollen yarn in Australia

Forecast a linear model with possible time series components

Multi-Seasonal Time Series

Forecasting using BATS and TBATS models

forecast.StructTS

Forecasting using Structural Time Series models

Naive forecasts

Plot components from ETS model

Forecasting using ETS models

Get response variable from time series model.

Plot components from BATS model

Neural Network Time Series Forecasts

Subsetting a time series

Random Walk Forecast

Australian monthly gas production

Daily morning gold prices

Exponential smoothing state space model