Summary of Gmp-ar: Granularity Message Passing and Adaptive Reconciliation For Temporal Hierarchy Forecasting, by Fan Zhou et al.
GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting
by Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, James Zhang, Jun Zhou, Hongyuan Mei, Weitao Lin, Zi Zhuang, Wenxin Ning, Yunhua Hu, Siqiao Xue
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for time series forecasting is proposed that leverages temporal hierarchy information to improve forecasting performance while maintaining coherence across different granularities. The granularity message-passing mechanism (GMP) and adaptive reconciliation (AR) strategy are designed to ensure alignment with downstream decisions, and an optimization module is introduced to achieve task-based targets under real-world constraints. Experimental results on real-world datasets demonstrate the superiority of the proposed framework (GMP-AR) over state-of-the-art methods in temporal hierarchical forecasting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists created a new way to predict future events based on patterns from the past. They wanted to make sure that their predictions were consistent and accurate at different levels, like predicting daily or weekly sales. They developed two new techniques: granularity message-passing mechanism (GMP) and adaptive reconciliation (AR). These tools help ensure that their forecasts are correct and useful for making decisions. The scientists tested their approach on real-world data and found that it worked better than other methods. |
Keywords
* Artificial intelligence * Alignment * Optimization * Time series
