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forecast (version 4.7)

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

4.7

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

September 27th, 2013

Functions in forecast (4.7)

Fit ARIMA model to univariate time series

Get response variable from time series model.

forecast.StructTS

Forecasting using Structural Time Series models

Seasonal adjustment

forecast.HoltWinters

Forecasting using Holt-Winters objects

Log-Likelihood of an ets object

Naive forecasts

Random Walk Forecast

Plot components from BATS model

Accuracy measures for forecast model

Seasonal dummy variables

Box Cox Transformation

Number of differences required for a stationary series

Cubic Spline Forecast

Exponential smoothing forecasts

Subsetting a time series

Simulation from a time series model

Neural Network Time Series Forecasts

tbats.components

Extract components of a TBATS model

Double-Seasonal Holt-Winters Forecasting

Forecast a linear model with possible time series components

(Partial) Autocorrelation Function Estimation

Theta method forecast

Time series display

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

Australian total wine sales

Forecasting using stl objects

Fit best ARIMA model to univariate time series

Cross-validation statistic

Fit a fractionally differenced ARFIMA model

Forecasting using ETS models

Exponential smoothing state space model

Daily morning gold prices

Diebold-Mariano test for predictive accuracy

Half-hourly electricity demand

Number of days in each season

Plot components from ETS model

Forecast seasonal index

Fit a linear model with time series components

Forecasts for intermittent demand using Croston's method

Multi-Seasonal Time Series

Australian monthly gas production

Interpolate missing values in a time series

Quarterly production of woollen yarn in Australia

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

One-step in-sample forecasts using ARIMA models

Moving-average smoothing

Automatic selection of Box Cox transformation parameter

Forecasting using BATS and TBATS models

Forecasting using ARIMA or ARFIMA models

Forecasting time series