Summary of Toto: Time Series Optimized Transformer For Observability, by Ben Cohen et al.
Toto: Time Series Optimized Transformer for Observability
by Ben Cohen, Emaad Khwaja, Kan Wang, Charles Masson, Elise Ramé, Youssef Doubli, Othmane Abou-Amal
First submitted to arxiv on: 10 Jul 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 Time Series Optimized Transformer for Observability (Toto) is a novel foundation model for time series forecasting developed by Datadog. This state-of-the-art model not only advances generalized time series benchmarks in domains like electricity and weather but also sets a new standard for observability metrics. By leveraging transformer-based architectures, Toto optimizes performance on various time series forecasting tasks while achieving impressive results on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a powerful tool called Time Series Optimized Transformer for Observability (Toto), which is a special kind of model that helps predict future events based on past data. Datadog created this new model to make it better at forecasting time series data, like the weather or energy usage. This means Toto can help us understand and prepare for future events more accurately. |
Keywords
* Artificial intelligence * Time series * Transformer
