Streamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management

TL;DR

This paper introduces a flexible framework for developing and selecting forecasting models efficiently, enhancing inventory management by streamlining data processing, experimentation, and evaluation processes.

Contribution

It presents a novel, streamlined framework that integrates data preprocessing, multiple validation strategies, and diverse evaluation metrics for robust forecasting model development.

Findings

Effective integration of datasets and algorithms

Improved model robustness through multiple validation strategies

Successful application in inventory management scenarios

Abstract

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.