Summary of Fomo: a Foundation Model For Mobile Traffic Forecasting with Diffusion Model, by Haoye Chai et al.
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model
by Haoye Chai, Xiaoqian Qi, Shiyuan Zhang, Yong Li
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Foundation model for Mobile traffic forecasting (FoMo) aims to revolutionize the field of mobile traffic prediction by developing a multi-tasking model that can handle various forecasting tasks, including short-term and long-term predictions, distribution generation, and task-agnostic learning. By combining diffusion models and transformers, FoMo learns intrinsic features of different tasks and captures correlations between mobile traffic and urban contexts through a contrastive learning strategy. This paper demonstrates the effectiveness of FoMo in diverse forecasting tasks and zero/few-shot learning on 9 real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FoMo is a new way to predict how much data will be sent over mobile networks, which can help make sure that phones and devices have good connections. Right now, there are only models that work well for specific jobs, like predicting traffic patterns in one city. But FoMo is different because it’s designed to work on many different tasks at the same time and even learn new things without needing lots of training data. |
Keywords
» Artificial intelligence » Diffusion » Few shot
