Summary of A Comprehensive Forecasting Framework Based on Multi-stage Hierarchical Forecasting Reconciliation and Adjustment, by Zhengchao Yang et al.
A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment
by Zhengchao Yang, Mithun Ghosh, Anish Saha, Dong Xu, Konstantin Shmakov, Kuang-chih Lee
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to demand forecasting for Walmart’s ad products, addressing the challenges of hierarchical time series forecasting in business settings. The proposed “Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment” (HiFoReAd) framework combines diverse models through Bayesian Optimization (BO), achieving base forecasts that are then refined using Top-Down forecasts, MinTrace algorithm, harmonic alignment, and stratified scaling. Experimental results on Walmart’s internal Ads-demand dataset and three public datasets demonstrate significant improvements in accuracy (3% to 40% across levels) compared to ensembles of models (LGBM, MSTL+ETS, Prophet) and State-Of-The-Art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to predict demand for ads at Walmart. It’s like trying to guess how many people will buy things based on past sales. The authors use special techniques called “ensembles” that combine different predictions together. They also develop their own method, called HiFoReAd, which helps make the forecasts more accurate and consistent. The results show that this approach is much better than other methods at predicting demand, which can help Walmart make better decisions about advertising. |
Keywords
» Artificial intelligence » Alignment » Optimization » Time series
