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forecast (version 4.01)

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

4.01

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

January 22nd, 2013

Functions in forecast (4.01)

Accuracy measures for forecast model

forecast.HoltWinters

Forecasting using Holt-Winters objects

Forecasting using ETS models

Fit ARIMA model to univariate time series

Plot components from ETS model

(Partial) Autocorrelation Function Estimation

Number of days in each season

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

Seasonal dummy variables

Box Cox Transformation

Cross-validation statistic

Get response variable from time series model.

Multi-Seasonal Time Series

Double-Seasonal Holt-Winters Forecasting

Subsetting a time series

Forecast seasonal index

Seasonal adjustment

Forecasting using stl objects

Forecasting using ARIMA or ARFIMA models

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

Time series display

Forecast a linear model with possible time series components

Simulation from a time series model

Automatic selection of Box Cox transformation parameter

Fit a linear model with time series components

Australian monthly gas production

One-step in-sample forecasts using ARIMA models

Naive forecasts

Fit best ARIMA model to univariate time series

Plot components from BATS model

Exponential smoothing forecasts

Half-hourly electricity demand

Forecasts for intermittent demand using Croston's method

forecast.StructTS

Forecasting using Structural Time Series models

Exponential smoothing state space model

Neural Network Time Series Forecasts

Random Walk Forecast

Australian total wine sales

Cubic Spline Forecast

Forecasting using BATS and TBATS models

Moving-average smoothing

Log-Likelihood of an ets object

Number of differences required for a stationary series

Theta method forecast

Daily morning gold prices

Interpolate missing values in a time series

Fit a fractionally differenced ARFIMA model

Quarterly production of woollen yarn in Australia

Diebold-Mariano test for predictive accuracy

Forecasting time series