Summary of Transformers with Sparse Attention For Granger Causality, by Riya Mahesh et al.
Transformers with Sparse Attention for Granger Causality
by Riya Mahesh, Rahul Vashisht, Chandrashekar Lakshminarayanan
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 In this paper, researchers propose a novel deep learning-based method for temporal causal analysis, building upon recent advances in transformer models. The approach, called Sparse Attention Transformer (SAT), leverages self-attention weights to identify causal links between variables in multivariate time-series data with varying lag dependencies. SAT combines temporal attention and variable attention to compute Granger Causality indices, outperforming traditional Vector Autoregression-based methods that assume fixed lag lengths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to analyze the causes behind things changing over time. It uses special kinds of artificial intelligence called transformers to find connections between different variables. The method is good at finding important past events that affect what happens next. This is useful for many applications, such as predicting stock prices or understanding how climate change affects weather patterns. |
Keywords
» Artificial intelligence » Attention » Deep learning » Self attention » Time series » Transformer
