Hierarchical Time Series Forecasting using Singular Spectrum Analysis • ssa.hts

The goal of the ssa.hts package is to use singular spectrum analysis to forecast hierarchical time series.

Installation

You can install the development version of ssa.hts from GitHub with:

# install.packages("devtools")
devtools::install_github("paulocanas/ssa.hts")

Example

This is a basic example which shows you how to solve a common problem:

#library(ssa.hts)
## basic example code
x <- datasets::AirPassengers[1:15]
L <- 8
ssa.hts::hankel_matrix(x, L)
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,]  112  118  132  129  121  135  148  148
#> [2,]  118  132  129  121  135  148  148  136
#> [3,]  132  129  121  135  148  148  136  119
#> [4,]  129  121  135  148  148  136  119  104
#> [5,]  121  135  148  148  136  119  104  118
#> [6,]  135  148  148  136  119  104  118  115
#> [7,]  148  148  136  119  104  118  115  126
#> [8,]  148  136  119  104  118  115  126  141

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

#You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this.

Plot of the data:

#In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.