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forecast (version 5.6)

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

205,453

Version

5.6

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

September 24th, 2014

Functions in forecast (5.6)

Diebold-Mariano test for predictive accuracy

Number of trading days in each season

Forecasting using ARIMA or ARFIMA models

Forecasting time series

Forecasting using ETS models

Multi-Seasonal Time Series

Neural Network Time Series Forecasts

Log-Likelihood of an ets object

Fit a fractionally differenced ARFIMA model

Subsetting a time series

Australian total wine sales

Plot components from BATS model

Identify and replace outliers in a time series

Easter holidays in each season

Cross-validation statistic

Interpolate missing values in a time series

Random Walk Forecast

Fit a linear model with time series components

Fit best ARIMA model to univariate time series

Double-Seasonal Holt-Winters Forecasting

Naive forecasts

tbats.components

Extract components of a TBATS model

(Partial) Autocorrelation Function Estimation

Box Cox Transformation

Theta method forecast

Simulation from a time series model

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

forecast.StructTS

Forecasting using Structural Time Series models

Seasonal dummy variables

Exponential smoothing state space model

Identify and replace outliers and missing values in a time series

Return the order of an ARIMA or ARFIMA model

Exponential smoothing forecasts

Forecasting using stl objects

Plot components from ETS model

Accuracy measures for forecast model

One-step in-sample forecasts using ARIMA models

Australian monthly gas production

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

Forecast seasonal index

Half-hourly electricity demand

forecast.HoltWinters

Forecasting using Holt-Winters objects

Get response variable from time series model.

Automatic selection of Box Cox transformation parameter

Forecasts for intermittent demand using Croston's method

Cubic Spline Forecast

Daily morning gold prices

Number of differences required for a stationary series

Forecasting using BATS and TBATS models

Moving-average smoothing

Time series display

Quarterly production of woollen yarn in Australia

Number of days in each season

Forecast a linear model with possible time series components

Seasonal adjustment

Fit ARIMA model to univariate time series