Explainable Demand Forecasting: A Data Mining Goldmine

Abstract

Demand forecasting is a crucial component of demand management. Value is provided to the organization through accurate forecasts and insights into the reasons driving the forecasts to increase confidence and assist decision-making. In this Ph.D., we aim to develop state-of-the-art demand forecasting models for irregular demand, develop explainability mechanisms to avoid exposing models fine-grained information regarding the model features, create a recommender system to assist users on decision-making and develop mechanisms to enrich knowledge graphs with feedback provided by the users through artificial intelligence-powered feedback modules. We have already developed models for accurate forecasts regarding steady and irregular demand and architecture to provide forecast explanations that preserve sensitive information regarding model features. These explanations highlighting real-world events that provide insights on the general context captured through the dataset features while highlighting actionable items and suggesting datasets for future data enrichment.