Summary of Autostf: Decoupled Neural Architecture Search For Cost-effective Automated Spatio-temporal Forecasting, by Tengfei Lyu et al.
AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting
by Tengfei Lyu, Weijia Zhang, Jinliang Deng, Hao Liu
First submitted to arxiv on: 25 Sep 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 This paper proposes a novel framework for automated spatio-temporal forecasting called AutoSTF, which addresses the limitations of existing approaches by decoupling the search space into temporal and spatial domains. To improve efficiency, AutoSTF employs representation compression and parameter-sharing schemes to mitigate the impact of neural architecture search overhead. Additionally, the framework introduces a multi-patch transfer module that captures multi-granularity temporal dependencies and enables finer-grained layer-wise spatial dependency search. Evaluation on eight datasets demonstrates the superiority of AutoSTF in terms of both accuracy and efficiency, achieving up to 13.48x speed-up compared to state-of-the-art methods while maintaining top forecasting accuracy. The paper’s contributions have significant implications for smart city applications such as transportation optimization, energy management, and socio-economic analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve how we predict future events that involve both space (like locations) and time (like days or hours). They want to make this process faster and more accurate. To do this, they created a new system called AutoSTF that breaks down the problem into smaller parts and uses special techniques to solve each part quickly. The result is a system that can predict events much faster than existing methods while still being just as good at making accurate predictions. This could be very useful for cities, where we need to make smart decisions about things like traffic and energy usage. |
Keywords
* Artificial intelligence * Optimization
