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forecast (version 6.2)

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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Install

install.packages('forecast')

Monthly Downloads

191,858

Version

6.2

License

GPL (>= 2)

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Repository

http://github.com/robjhyndman/forecast

Maintainer

Rob Hyndman

Last Published

October 20th, 2015

Functions in forecast (6.2)

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

Plot characteristic roots from ARIMA model

Accuracy measures for forecast model

Number of trading days in each season

Forecasting using ETS models

Diebold-Mariano test for predictive accuracy

Double-Seasonal Holt-Winters Forecasting

Neural Network Time Series Forecasts

Get response variable from time series model.

Interpolate missing values in a time series

Exponential smoothing forecasts

Exponential smoothing state space model

Box Cox Transformation

Fit ARIMA model to univariate time series

Multi-Seasonal Time Series

Cross-validation statistic

Automatic selection of Box Cox transformation parameter

Return the order of an ARIMA or ARFIMA model

Moving-average smoothing

Plot components from BATS model

Forecast a linear model with possible time series components

Forecasting using BATS and TBATS models

Cubic Spline Forecast

Easter holidays in each season

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

Fit best ARIMA model to univariate time series

Theta method forecast

Naive forecasts

Forecasting using stl objects

Identify and replace outliers and missing values in a time series

Number of days in each season

forecast.StructTS

Forecasting using Structural Time Series models

Forecasting time series

Plot components from ETS model

Forecast seasonal index

Daily morning gold prices

(Partial) Autocorrelation Function Estimation

Number of differences required for a stationary series

Identify and replace outliers in a time series

Forecasts for intermittent demand using Croston's method

Fit a linear model with time series components

Find dominant frequency of a time series

Simulation from a time series model

tbats.components

Extract components of a TBATS model

Seasonal adjustment

Half-hourly electricity demand

Seasonal dummy variables

Random Walk Forecast

Subsetting a time series

Australian monthly gas production

One-step in-sample forecasts using ARIMA models

Log-Likelihood of an ets object

Fit a fractionally differenced ARFIMA model

Time series display

Quarterly production of woollen yarn in Australia

forecast.HoltWinters

Forecasting using Holt-Winters objects

Forecasting using ARIMA or ARFIMA models

Australian total wine sales