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sufficientForecasting

Overview

The goal of sufficientForecasting is to forecast a single time series when there is a large number of predictors and a possible nonlinear effect.

Installation

You can install the development version of sufficientForecasting like so:

# The easiest way to install sufficientForecasting
install.packages("sufficientForecasting")
# OR
devtools::install_github("JingFu1224/sufficientForecasting")

Usage

The following example uses SF.CI to solve a problem: forecast a single time series, and its upper bound and lower bound

library(sufficientForecasting)
## basic example code
SF.CI(y=dataExample$y,X=dataExample$X,newX=dataExample$newX,type="LLM",alpha = 0.05)
#>     yhat ci_lower ci_upper 
#>  -0.3568  -2.4740   1.6076

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Version

Install

install.packages('sufficientForecasting')

Monthly Downloads

197

Version

0.1.0

License

GPL (>= 3)

Issues

Pull Requests

Stars

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Maintainer

Jing Fu

Last Published

February 17th, 2023

Functions in sufficientForecasting (0.1.0)

getK

Estimate the number of common factors K
SF.SIR

Sliced inverse regression for sufficient forecasting
SF.DR

Directional regression for sufficient forecasting
SF.CI

Conformal inference of the sufficient forecasting
sufficientForecasting-package

sufficientForecasting: Sufficient Forecasting using Factor Models
dataExample

A simulated dataset
SF.PC

Principal component regression for sufficient forecasting
SF

Select a method from PC, SIR and DR to do point prediction