Summary of Tivat: a Transformer with a Single Unified Mechanism For Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting, by Junwoo Ha et al.
TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting
by Junwoo Ha, Hyukjae Kwon, Sungsoo Kim, Kisu Lee, Seungjae Park, Ha Young Kim
First submitted to arxiv on: 2 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 This paper proposes a novel architecture called TiVaT (Time-variate Transformer) for multivariate time series (MTS) forecasting. The model addresses the challenge of simultaneously modeling temporal and inter-variate dependencies by introducing a joint-axis attention module that processes these dependencies concurrently. This module is designed to capture complex interactions such as lead-lag dynamics. Additionally, the paper introduces distance-aware time-variate sampling in the joint-axis attention module, which extracts significant patterns through a learned 2D embedding space while reducing noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new model called TiVaT that helps predict multiple things over time. It’s hard to do this because you need to consider both how things change over time and how they relate to each other. The new model has a special part that looks at these relationships all at once, which makes it better at predicting complex patterns. |
Keywords
» Artificial intelligence » Attention » Embedding space » Time series » Transformer
