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forecast (version 7.0)

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

142,544

Version

7.0

License

GPL (>= 2)

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Repository

http://github.com/robjhyndman/forecast

Maintainer

Rob Hyndman

Last Published

April 3rd, 2016

Functions in forecast (7.0)

ggplot STL object

Number of differences required for a stationary series

Cross-validation statistic

Exponential smoothing state space model

Forecasting using BATS and TBATS models

Log-Likelihood of an ets object

Fit best ARIMA model to univariate time series

ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation

Forecasting using ARIMA or ARFIMA models

Half-hourly electricity demand

Quarterly production of woollen yarn in Australia

Theta method forecast

Identify and replace outliers and missing values in a time series

Accuracy measures for forecast model

One-step in-sample forecasts using ARIMA models

Is an object a particular model type?

Forecast a linear model with possible time series components

Time series display

Multi-Seasonal Time Series

Is an object a particular forecast type?

Fit a fractionally differenced ARFIMA model

Box Cox Transformation

Plot components from BATS model

Find dominant frequency of a time series

Create a seasonal subseries ggplot

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

autoplot.decomposed.ts

ggplot of a decomposed time series object

Exponential smoothing forecasts

forecast.HoltWinters

Forecasting using Holt-Winters objects

forecast.StructTS

Forecasting using Structural Time Series models

Forecasting time series

Plot components from ETS model

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

Daily morning gold prices

Random Walk Forecast

Cubic Spline Forecast

Automatic selection of Box Cox transformation parameter

Automatically create a ggplot for time series objects

Number of trading days in each season

Interpolate missing values in a time series

Forecasts for intermittent demand using Croston's method

Seasonal adjustment

Forecast a multiple linear model with possible time series components

Fit a linear model with time series components

Double-Seasonal Holt-Winters Forecasting

Is an object constant?

Australian total wine sales

Fit ARIMA model to univariate time series

Australian monthly gas production

Get response variable from time series model.

Moving-average smoothing

Multivariate forecast plot

Forecast seasonal index

Forecasting using ETS models

Plot characteristic roots from ARIMA model

Neural Network Time Series Forecasts

Return the order of an ARIMA or ARFIMA model

fortify.forecast

Fortify a forecast object to data.frame for ggplot

Simulation from a time series model

tbats.components

Extract components of a TBATS model

Identify and replace outliers in a time series

Easter holidays in each season

Forecasting time series

Forecasting using stl objects

Seasonal dummy variables

Subsetting a time series

Naive forecasts

(Partial) Autocorrelation and Cross-Correlation Function Estimation

Number of days in each season

Diebold-Mariano test for predictive accuracy