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forecast (version 5.8)

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

5.8

License

GPL (>= 2)

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Repository

http://github.com/robjhyndman/forecast

Maintainer

Rob Hyndman

Last Published

January 6th, 2015

Functions in forecast (5.8)

Forecasting using ETS models

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

Exponential smoothing state space model

Return the order of an ARIMA or ARFIMA model

Cross-validation statistic

Cubic Spline Forecast

Fit a linear model with time series components

forecast.HoltWinters

Forecasting using Holt-Winters objects

Subsetting a time series

Theta method forecast

Half-hourly electricity demand

Log-Likelihood of an ets object

Multi-Seasonal Time Series

Fit best ARIMA model to univariate time series

Plot components from ETS model

Identify and replace outliers and missing values in a time series

Interpolate missing values in a time series

Fit ARIMA model to univariate time series

Neural Network Time Series Forecasts

Identify and replace outliers in a time series

Forecasting time series

Exponential smoothing forecasts

Automatic selection of Box Cox transformation parameter

Forecasting using stl objects

Forecasts for intermittent demand using Croston's method

Quarterly production of woollen yarn in Australia

Time series display

Simulation from a time series model

Australian total wine sales

Australian monthly gas production

tbats.components

Extract components of a TBATS model

Seasonal dummy variables

Forecasting using ARIMA or ARFIMA models

Daily morning gold prices

Moving-average smoothing

Naive forecasts

Number of differences required for a stationary series

forecast.StructTS

Forecasting using Structural Time Series models

Easter holidays in each season

Fit a fractionally differenced ARFIMA model

Plot components from BATS model

Forecast seasonal index

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

Get response variable from time series model.

One-step in-sample forecasts using ARIMA models

Number of trading days in each season

Find dominant frequency of a time series

Diebold-Mariano test for predictive accuracy

Seasonal adjustment

Number of days in each season

Accuracy measures for forecast model

Box Cox Transformation

Random Walk Forecast

(Partial) Autocorrelation Function Estimation

Forecasting using BATS and TBATS models

Forecast a linear model with possible time series components

Double-Seasonal Holt-Winters Forecasting