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forecast (version 5.3)

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

137,850

Version

5.3

License

GPL (>= 2)

Maintainer

Rob Hyndman

Last Published

March 24th, 2014

Functions in forecast (5.3)

Number of days in each season

Australian total wine sales

Exponential smoothing forecasts

Box Cox Transformation

Interpolate missing values in a time series

(Partial) Autocorrelation Function Estimation

Australian monthly gas production

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

Plot components from BATS model

Daily morning gold prices

Forecasting using BATS and TBATS models

Theta method forecast

Number of differences required for a stationary series

Easter holidays in each season

Exponential smoothing state space model

Accuracy measures for forecast model

One-step in-sample forecasts using ARIMA models

Forecasting using stl objects

Double-Seasonal Holt-Winters Forecasting

Fit a fractionally differenced ARFIMA model

Subsetting a time series

Forecast a linear model with possible time series components

Moving-average smoothing

Half-hourly electricity demand

Plot components from ETS model

Quarterly production of woollen yarn in Australia

Fit ARIMA model to univariate time series

Log-Likelihood of an ets object

Naive forecasts

Seasonal dummy variables

Identify and replace outliers and missing values in a time series

tbats.components

Extract components of a TBATS model

Diebold-Mariano test for predictive accuracy

Forecasting using ARIMA or ARFIMA models

Forecasting time series

Time series display

Automatic selection of Box Cox transformation parameter

Multi-Seasonal Time Series

Seasonal adjustment

Forecast seasonal index

Number of trading days in each season

forecast.StructTS

Forecasting using Structural Time Series models

Neural Network Time Series Forecasts

Fit best ARIMA model to univariate time series

Get response variable from time series model.

Identify and replace outliers in a time series

Return the order of an ARIMA or ARFIMA model

Fit a linear model with time series components

forecast.HoltWinters

Forecasting using Holt-Winters objects

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

Forecasting using ETS models

Simulation from a time series model

Cross-validation statistic

Forecasts for intermittent demand using Croston's method

Cubic Spline Forecast

Random Walk Forecast