Summary of Stdcformer: a Transformer-based Model with a Spatial-temporal Causal De-confounding Strategy For Crowd Flow Prediction, by Silu He et al.
STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal De-Confounding Strategy for Crowd Flow Prediction
by Silu He, Peng Shen, Pingzhen Xu, Qinyao Luo, Haifeng Li
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach to spatial-temporal prediction is proposed in this paper, decomposing it into three processes: encoding, cross-time mapping, and decoding. The authors suggest that traditional methods treat spatial-temporal prediction as a single function F, while their method breaks it down into E-M-D (encoding, cross-time mapping, decoding). This leads to two key questions: Q1, what kind of representation space allows for mapping the past to the future? And Q2, how to achieve this mapping within the representation space? To address these questions, the authors propose a Spatial-Temporal Backdoor Adjustment strategy and a Spatial-Temporal Embedding (STE) that captures spatial-temporal characteristics. Additionally, they introduce a Cross-Time Attention mechanism to guide spatial-temporal mapping. This paper’s approach is demonstrated to be effective in learning causal relationships between historical and future data, enabling better spatial-temporal prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spatial-temporal prediction is like trying to guess what will happen tomorrow based on what happened yesterday. The current way of doing this is treating it as one big problem. But the authors suggest breaking it down into smaller parts: encoding, cross-time mapping, and decoding. They want to know what kind of space allows us to connect past and future, and how to do that. To solve this, they propose a new method called Spatial-Temporal Backdoor Adjustment and another one called Spatial-Temporal Embedding. This helps them learn what causes things to happen in the past that affect the future. |
Keywords
» Artificial intelligence » Attention » Embedding
