| Type: | Package |
| Title: | The Use of Marginal Distributions in Conditional Forecasting |
| Version: | 0.1.1 |
| Author: | Mohamad-Taher Anan [aut], Mohamad Alawad [aut], Bushra Alsaeed [aut, cre] |
| Maintainer: | Bushra Alsaeed <alsaeedbushra41@gmail.com> |
| Description: | A new way to predict time series using the marginal distribution table in the absence of the significance of traditional models. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Imports: | tibble |
| NeedsCompilation: | no |
| Packaged: | 2023-01-05 17:17:45 UTC; MB |
| Repository: | CRAN |
| Date/Publication: | 2023-01-06 21:30:06 UTC |
The Use of Marginal Distributions in Conditional Forecasting
Description
A new way to predict time series using the marginal distribution table in the absence of the significance of traditional models.
Usage
ff(dt,m,w,n,q1)
Arguments
dt |
data frame |
m |
the number of time series |
w |
the number of predicted values |
n |
number of values |
q1 |
matrix independent time series values #In the case of m=2, enter the independent string values as follows(matrix(c())),In the case of m=3, enter the independent string values as follows(matrix(c(),w,m-1,byrow=T)) |
Value
the output from ff()
Examples
x=rnorm(17,10,1)
y=rnorm(17,10,1)
data=data.frame(x,y)
print("Enter independent time series values")
q1=list(q=matrix(c(scan(,,quiet=TRUE)),1,2-1))
10.5
ff(data,2,1,17,q1)