# Streamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management

arXiv: https://arxiv.org/abs/2304.06344 · HTML: https://mlsanity.com/paper/2304.06344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [Evaluating time series forecasting models: An empirical study on performance estimation methods](https://mlsanity.com/paper/1905.11744/) (2019)
- [AR-Net: A simple Auto-Regressive Neural Network for time-series](https://mlsanity.com/paper/1911.12436/) (2019)
- [Forecasting for Social Good](https://mlsanity.com/paper/2009.11669/) (2020)

## Abstract (arXiv)

This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new datasets, experimentation on different algorithms, and selection of the best models. We start with the datasets of different issues and apply pre-processing steps to clean and engineer meaningful representations of time-series data. To identify robust training configurations, we introduce a novel mechanism of multiple cross-validation strategies. We apply different evaluation metrics to find the best-suited models for varying applications. One of the referent applications is our participation in the intelligent forecasting competition held by the United States Agency of International Development (USAID). Finally, we leverage the flexibility of the framework by applying different evaluation metrics to assess the performance of the models in inventory management settings.