Summary of Comparing Prior and Learned Time Representations in Transformer Models Of Timeseries, by Natalia Koliou et al.
Comparing Prior and Learned Time Representations in Transformer Models of Timeseries
by Natalia Koliou, Tatiana Boura, Stasinos Konstantopoulos, George Meramveliotakis, George Kosmadakis
First submitted to arxiv on: 19 Nov 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 The proposed study investigates two variations of the Transformer architecture for timeseries analysis, where the time representation is either fixed or learned from data. The experiments use solar panel energy output prediction as a test case, which exhibits periodicities that can be easily encoded in the fixed time representation. However, the results suggest that it is challenging to encode prior knowledge due to side effects that are difficult to mitigate. Therefore, more research is needed to develop ways to incorporate human feedback into the learning process and improve the robustness and trustworthiness of the network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores two different Transformer models for timeseries analysis. One model uses a fixed time representation, while the other learns its time representation from the data. The researchers used solar panel energy output prediction as an example task that has daily and seasonal patterns. They found it hard to include prior knowledge because of unwanted effects. This shows that we need more research on how humans can help AI learn better. |
Keywords
» Artificial intelligence » Transformer
