Summary of Generalizable Autoregressive Modeling Of Time Series Through Functional Narratives, by Ran Liu et al.
Generalizable autoregressive modeling of time series through functional narratives
by Ran Liu, Wenrui Ma, Ellen Zippi, Hadi Pouransari, Jingyun Xiao, Chris Sandino, Behrooz Mahasseni, Juri Minxha, Erdrin Azemi, Eva L. Dyer, Ali Moin
First submitted to arxiv on: 10 Oct 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 Medium Difficulty summary: This paper proposes a novel approach to learning time series data using transformers, which typically model time series as concatenations of time periods. The authors introduce an objective that re-interprets time series as temporal functions and builds an alternative sequence by creating augmented variants with different degrees of simplification. They train an autoregressive transformer to progressively recover the original sample from the most simplified variant, aiming to learn the Narratives of Time Series (NoTS) by connecting different functions in time. The authors demonstrate the advantages of this approach through theoretical justifications and experimental results, showing a 26% performance improvement in synthetic feature regression experiments and up to 6% outperformance on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper is about improving how computers learn from time series data. Time series data are like lists of numbers that change over time, but current methods don’t take into account the fact that these lists are actually functions of time. The authors propose a new way to learn from this type of data by treating it as a sequence of functions that can be connected to form a narrative. They show that this approach is more effective than traditional methods, especially when dealing with complex datasets. This has potential applications in fields like finance and healthcare. |
Keywords
» Artificial intelligence » Autoregressive » Regression » Time series » Transformer
