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forecast (version 6.0)

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

137,850

Version

6.0

License

GPL (>= 2)

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Repository

http://github.com/robjhyndman/forecast

Maintainer

Rob Hyndman

Last Published

May 9th, 2015

Functions in forecast (6.0)

Moving-average smoothing

Daily morning gold prices

Number of days in each season

Automatic selection of Box Cox transformation parameter

Theta method forecast

Time series display

Forecasting using stl objects

Diebold-Mariano test for predictive accuracy

Identify and replace outliers and missing values in a time series

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

Fit ARIMA model to univariate time series

Forecast seasonal index

Neural Network Time Series Forecasts

Easter holidays in each season

Simulation from a time series model

Forecasting using ARIMA or ARFIMA models

Find dominant frequency of a time series

Cubic Spline Forecast

Forecasting using ETS models

Plot characteristic roots from ARIMA model

tbats.components

Extract components of a TBATS model

Exponential smoothing state space model

Plot components from ETS model

forecast.HoltWinters

Forecasting using Holt-Winters objects

Fit a linear model with time series components

Subsetting a time series

Return the order of an ARIMA or ARFIMA model

Forecasts for intermittent demand using Croston's method

Box Cox Transformation

Cross-validation statistic

forecast.StructTS

Forecasting using Structural Time Series models

Number of trading days in each season

Seasonal adjustment

Exponential smoothing forecasts

Forecast a linear model with possible time series components

One-step in-sample forecasts using ARIMA models

Random Walk Forecast

Double-Seasonal Holt-Winters Forecasting

Fit a fractionally differenced ARFIMA model

Australian total wine sales

Quarterly production of woollen yarn in Australia

Identify and replace outliers in a time series

Number of differences required for a stationary series

Get response variable from time series model.

Plot components from BATS model

Log-Likelihood of an ets object

Naive forecasts

(Partial) Autocorrelation Function Estimation

Multi-Seasonal Time Series

Half-hourly electricity demand

Seasonal dummy variables

Fit best ARIMA model to univariate time series

Forecasting using BATS and TBATS models

Australian monthly gas production

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

Interpolate missing values in a time series

Forecasting time series

Accuracy measures for forecast model