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forecast (version 5.2)

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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Version

Install

install.packages('forecast')

Monthly Downloads

191,858

Version

5.2

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

February 24th, 2014

Functions in forecast (5.2)

Fit best ARIMA model to univariate time series

Forecast a linear model with possible time series components

One-step in-sample forecasts using ARIMA models

Box Cox Transformation

Cross-validation statistic

Fit ARIMA model to univariate time series

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)

Theta method forecast

Simulation from a time series model

Accuracy measures for forecast model

Get response variable from time series model.

Multi-Seasonal Time Series

Number of days in each season

Double-Seasonal Holt-Winters Forecasting

Easter holidays in each season

Seasonal adjustment

Moving-average smoothing

Half-hourly electricity demand

Forecasting using stl objects

Forecasting using BATS and TBATS models

Australian monthly gas production

Fit a linear model with time series components

Forecast seasonal index

tbats.components

Extract components of a TBATS model

forecast.HoltWinters

Forecasting using Holt-Winters objects

Neural Network Time Series Forecasts

Exponential smoothing forecasts

Number of differences required for a stationary series

Forecasting using ARIMA or ARFIMA models

Fit a fractionally differenced ARFIMA model

Number of trading days in each season

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

Automatic selection of Box Cox transformation parameter

Diebold-Mariano test for predictive accuracy

Plot components from BATS model

Forecasts for intermittent demand using Croston's method

Identify and replace outliers and missing values in a time series

Subsetting a time series

Naive forecasts

Plot components from ETS model

Forecasting using ETS models

Exponential smoothing state space model

Seasonal dummy variables

Quarterly production of woollen yarn in Australia

Time series display

(Partial) Autocorrelation Function Estimation

Log-Likelihood of an ets object

Interpolate missing values in a time series

Cubic Spline Forecast

Random Walk Forecast

Identify and replace outliers in a time series

Australian total wine sales

Daily morning gold prices

Forecasting time series