fide provides a feature-based forecasting method for intermittent demand proposed by Li Li, Yanfei Kang, Fotios Petropoulos, and Feng Li. The package aims to facilitate reproducing the results of our paper, and can also be applied to other intermittent demand forecasting problems.
You can install fide from github with:
devtools::install_github("lily940703/fide")
library(M4metalearning)
library(tsintermittent)
library(fide)
We provide simulated data and two real datasets for intermittent demand forecasting, please see this page for details.
An example of using the package based on simulated data is shown on this page.
- Li, Li, Yanfei Kang, Fotios Petropoulos, and Feng Li (2023). "Feature-based Intermittent Demand Forecast Combinations: Accuracy and Inventory Implications." International Journal of Production Research, 61(22), pp. 7557-7572.
@article{LiL2023FeaturebasedIntermittent,
title = {Feature-based intermittent demand forecast combinations: accuracy and inventory implications},
volume = {61},
url = {https://arxiv.org/abs/2204.08283},
doi = {10.1080/00207543.2022.2153941},
pages = {7557--7572},
number = {22},
journaltitle = {International Journal of Production Research},
author = {Li, Li and Kang, Yanfei and Petropoulos, Fotios and Li, Feng},
date = {2023-11},
}