Zexing Xu, Linjun Zhang, Sitan Yang, Rasoul Etesami, Hanghang Tong, Huan Zhang, Jiawei Han
arXiv 23 Jun 2024 · Machine Learning · 2 citations (OpenAlex)
arXiv:2406.16221 · PDF · DOI · OpenAlex · Extracted main text
Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data from these peak periods poses a significant challenge for traditional forecasting methods. In this paper, we propose a novel approach that leverages strategically chosen proxy data reflective of potential sales patterns from similar entities during non-peak periods, enriched by features learned from a graph neural networks (GNNs)-based forecasting model, to predict demand during peak events. We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm that leverages proxy data from non-peak periods and GNN-generated relational metadata to learn feature-specific layer parameters, thereby adapting to demand forecasts for peak events. Theoretically, we show that by considering domain similarities through task-specific metadata, our model achieves improved generalization, where the excess risk decreases as the number of training tasks increases. Empirical evaluations on large-scale industrial datasets demonstrate the superiority of our approach. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
appendix boundary found by appendix_command · 65% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Alex Nichol, Joshua Achiam, and John Schulman (2018) On First-Order Meta-Learning Algorithms | 0.843 | 4 | 4 | 75% |
| 2 | Sitan Yang, Malcolm Wolff, Shankar Ramasubramanian, Vincent Quennevi… (2023) GEANN: Scalable graph augmentations for multi-horizon time series forecasting. In KDD 2023 Workshop on Deep Learning on Graphs self | 0.843 | 4 | 4 | 75% |
| 3 | Chelsea Finn, Pieter Abbeel, and Sergey Levine (2017) Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machin… | 0.644 | 2 | 2 | 100% |
| 4 | Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li (2017) Meta-sgd: Learning to learn quickly for few shot learning | 0.644 | 2 | 2 | 100% |
| 5 | Risto Vuorio, Shao-Hua Sun, Hexiang Hu, and Joseph J. Lim (2019) Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation. In Neural Information Processing Systems | 0.644 | 2 | 2 | 100% |
| 6 | Pan Zhou, Yingtian Zou, Xiaotong Yuan, Jiashi Feng, Caiming Xiong, a… (2020) Task Similarity Aware Meta Learning: Theory-inspired Improvement on MAML. In 4th Workshop on Meta-Learning at NeurIPS | 0.644 | 2 | 2 | 100% |
| 7 | Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio (2020) N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In Proceedings of the 8th International Conf… | 0.585 | 3 | 1 | 100% |
| 8 | Neo Wu, Bradley Green, Xue Ben, and Shawn O'Banion (2020) Deep transformer models for time series forecasting: The influenza prevalence case | 0.511 | 2 | 1 | 100% |
| 9 | Huaxiu Yao, Ying Wei, Junzhou Huang, and Zhenhui Li (2019) Learning to learn by remembering. In Advances in Neural Information Processing Systems. 1574–1584 | 0.511 | 2 | 1 | 100% |
| 10 | Ilya Sutskever, Oriol Vinyals, and Quoc V. Le (2014) Sequence to Sequence Learning with Neural Networks. In NIPS. 3104–3112 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 53 scored citations.