# Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study

arXiv: https://arxiv.org/abs/2303.07139 · HTML: https://mlsanity.com/paper/2303.07139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 104 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling](https://mlsanity.com/paper/1403.1452/) (2014)
- [On the role of benchmarking data sets and simulations in method comparison studies](https://mlsanity.com/paper/2208.01457/) (2022)
- [Travel Time Prediction using Tree-Based Ensembles](https://mlsanity.com/paper/2005.13818/) (2020)

## Built on by (most cited first, 2 of 2)

- [Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization](https://mlsanity.com/paper/2604.21567/) (2026)
- [SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting](https://mlsanity.com/paper/2610.04109/) (2026)

## Abstract (arXiv)

Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare various forecasting methods in terms of out of the box forecasting performance on a broad set of simulated time series. We simulate various linear and non-linear time series and look at the one step forecast performance of statistical learning methods.