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forecast (version 4.05)

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

4.05

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

June 19th, 2013

Functions in forecast (4.05)

Get response variable from time series model.

Half-hourly electricity demand

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

Box Cox Transformation

Naive forecasts

forecast.StructTS

Forecasting using Structural Time Series models

Cross-validation statistic

tbats.components

Extract components of a TBATS model

Forecasting using ARIMA or ARFIMA models

Automatic selection of Box Cox transformation parameter

Theta method forecast

One-step in-sample forecasts using ARIMA models

forecast.HoltWinters

Forecasting using Holt-Winters objects

Cubic Spline Forecast

Fit a fractionally differenced ARFIMA model

Fit a linear model with time series components

Number of days in each season

Forecasting using stl objects

Australian monthly gas production

Moving-average smoothing

Quarterly production of woollen yarn in Australia

Diebold-Mariano test for predictive accuracy

(Partial) Autocorrelation Function Estimation

Forecasts for intermittent demand using Croston's method

Forecasting using ETS models

Accuracy measures for forecast model

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

Plot components from ETS model

Forecast a linear model with possible time series components

Time series display

Forecasting using BATS and TBATS models

Plot components from BATS model

Forecast seasonal index

Number of differences required for a stationary series

Daily morning gold prices

Australian total wine sales

Log-Likelihood of an ets object

Interpolate missing values in a time series

Fit best ARIMA model to univariate time series

Fit ARIMA model to univariate time series

Multi-Seasonal Time Series

Random Walk Forecast

Seasonal adjustment

Exponential smoothing state space model

Double-Seasonal Holt-Winters Forecasting

Neural Network Time Series Forecasts

Forecasting time series

Seasonal dummy variables

Simulation from a time series model

Exponential smoothing forecasts

Subsetting a time series