A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment

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Zhengchao Yang, Mithun Ghosh, Anish Saha, Dong Xu, Konstantin Shmakov, Kuang-Chih Lee

Published: 2024, Last Modified: 30 Jan 2026IEEE Big Data 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Ads demand forecasting for Walmart Connect’s ad products plays a critical role in enabling effective resource planning, allocation, and overall management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles the hierarchical time series forecasting problem in business settings. Though traditional hierarchical reconciliation methods ensure forecasting coherence, they often trade off accuracy for coherence especially at lower levels and fail to capture the seasonality patterns unique to each time-series in the hierarchy. Thus, we propose a novel framework "Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment (Multi-Stage HiFoReAd)" to address the challenges of preserving seasonality, ensuring coherence, and improving forecasting accuracy. Our system first utilizes diverse modeling techniques, ensembled through Bayesian Optimization (BO), achieving individual base forecasts. The generated base forecasts are then passed into the Multi-Stage HiFoReAd framework. The initial stage refines the hierarchy using Top-Down forecasts and "harmonic alignment." The second stage aligns the higher levels’ forecasts using MinTrace algorithm, following which the last two levels undergo "harmonic alignment" and "stratified scaling", to eventually achieve accurate and coherent forecasts across the whole hierarchy. Our experiments on Walmart’s internal Ads-demand dataset and 3 other public datasets, each with 4 hierarchical levels, demonstrate that the average Absolute Percentage Error (APE) from the cross-validation sets improve from 3% to 40% accross levels against BO-ensemble of models (LGBM, MSTL+ETS, Prophet) as well as from 1.2% to 92.9% against State-Of-The-Art models. In addition, the forecasts between all hierarchical levels are proved to be coherent. The proposed framework has been deployed and leveraged by Walmart’s ads, sales and operations teams to track future demands, make informed decisions and plan resources strategically.