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forecast (version 4.06)

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

428,345

Version

4.06

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

June 30th, 2013

Functions in forecast (4.06)

Cross-validation statistic

Fit a linear model with time series components

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

Australian monthly gas production

Forecasting using BATS and TBATS models

Interpolate missing values in a time series

Subsetting a time series

Time series display

Random Walk Forecast

Number of days in each season

forecast.StructTS

Forecasting using Structural Time Series models

Daily morning gold prices

Plot components from BATS model

Automatic selection of Box Cox transformation parameter

Box Cox Transformation

Number of differences required for a stationary series

Exponential smoothing forecasts

Accuracy measures for forecast model

One-step in-sample forecasts using ARIMA models

Forecasts for intermittent demand using Croston's method

Naive forecasts

Fit ARIMA model to univariate time series

Exponential smoothing state space model

tbats.components

Extract components of a TBATS model

Simulation from a time series model

Forecast a linear model with possible time series components

(Partial) Autocorrelation Function Estimation

Seasonal dummy variables

Fit best ARIMA model to univariate time series

Moving-average smoothing

Cubic Spline Forecast

Australian total wine sales

forecast.HoltWinters

Forecasting using Holt-Winters objects

Log-Likelihood of an ets object

Forecasting using stl objects

Theta method forecast

Plot components from ETS model

Seasonal adjustment

Get response variable from time series model.

Multi-Seasonal Time Series

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

Forecasting using ETS models

Forecast seasonal index

Quarterly production of woollen yarn in Australia

Fit a fractionally differenced ARFIMA model

Forecasting using ARIMA or ARFIMA models

Half-hourly electricity demand

Neural Network Time Series Forecasts

Double-Seasonal Holt-Winters Forecasting

Diebold-Mariano test for predictive accuracy

Forecasting time series