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forecast (version 4.8)

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

137,850

Version

4.8

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

September 30th, 2013

Functions in forecast (4.8)

Forecasting using ARIMA or ARFIMA models

forecast.StructTS

Forecasting using Structural Time Series models

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

Fit ARIMA model to univariate time series

Half-hourly electricity demand

Exponential smoothing state space model

Plot components from ETS model

Double-Seasonal Holt-Winters Forecasting

Fit best ARIMA model to univariate time series

Daily morning gold prices

forecast.HoltWinters

Forecasting using Holt-Winters objects

Number of days in each season

Seasonal adjustment

Forecasting using ETS models

Interpolate missing values in a time series

tbats.components

Extract components of a TBATS model

Multi-Seasonal Time Series

Forecasting using BATS and TBATS models

Forecasting using stl objects

Simulation from a time series model

Box Cox Transformation

Diebold-Mariano test for predictive accuracy

One-step in-sample forecasts using ARIMA models

Forecast a linear model with possible time series components

Exponential smoothing forecasts

Subsetting a time series

Forecasting time series

(Partial) Autocorrelation Function Estimation

Forecasts for intermittent demand using Croston's method

Plot components from BATS model

Australian monthly gas production

Australian total wine sales

Neural Network Time Series Forecasts

Time series display

Automatic selection of Box Cox transformation parameter

Number of differences required for a stationary series

Log-Likelihood of an ets object

Naive forecasts

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

Cross-validation statistic

Cubic Spline Forecast

Quarterly production of woollen yarn in Australia

Forecast seasonal index

Random Walk Forecast

Get response variable from time series model.

Moving-average smoothing

Seasonal dummy variables

Fit a fractionally differenced ARFIMA model

Theta method forecast

Accuracy measures for forecast model

Fit a linear model with time series components