Streamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management
This work addresses the need for efficient forecasting model development in inventory management, but it appears incremental as it builds on existing methods with a focus on streamlining processes.
The paper tackles the problem of developing forecasting models for inventory management by proposing a framework that streamlines connections between core components, enabling swift integration of new datasets and algorithm experimentation, with results demonstrated through participation in a USAID competition.
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.