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forecast (version 5.1)

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

5.1

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

February 8th, 2014

Functions in forecast (5.1)

Fit ARIMA model to univariate time series

Exponential smoothing state space model

Quarterly production of woollen yarn in Australia

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

(Partial) Autocorrelation Function Estimation

Number of days in each season

Automatic selection of Box Cox transformation parameter

Easter holidays in each season

Seasonal adjustment

Forecast a linear model with possible time series components

tbats.components

Extract components of a TBATS model

Log-Likelihood of an ets object

One-step in-sample forecasts using ARIMA models

Identify and replace outliers and missing values in a time series

Neural Network Time Series Forecasts

Plot components from BATS model

Forecast seasonal index

Number of differences required for a stationary series

Daily morning gold prices

Interpolate missing values in a time series

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

Get response variable from time series model.

Identify and replace outliers in a time series

Forecasting using ETS models

Exponential smoothing forecasts

Cubic Spline Forecast

Random Walk Forecast

Australian total wine sales

Seasonal dummy variables

Moving-average smoothing

Subsetting a time series

Theta method forecast

Number of trading days in each season

Forecasting time series

Forecasting using stl objects

Forecasts for intermittent demand using Croston's method

Forecasting using BATS and TBATS models

Australian monthly gas production

Box Cox Transformation

Cross-validation statistic

Fit best ARIMA model to univariate time series

Fit a linear model with time series components

Diebold-Mariano test for predictive accuracy

forecast.HoltWinters

Forecasting using Holt-Winters objects

forecast.StructTS

Forecasting using Structural Time Series models

Plot components from ETS model

Naive forecasts

Fit a fractionally differenced ARFIMA model

Half-hourly electricity demand

Forecasting using ARIMA or ARFIMA models

Double-Seasonal Holt-Winters Forecasting

Multi-Seasonal Time Series

Simulation from a time series model

Time series display

Accuracy measures for forecast model