This Time is Different: An Observability Perspective on Time Series Foundation Models
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908628823310336 |
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| author | Cohen, Ben Khwaja, Emaad Doubli, Youssef Lemaachi, Salahidine Lettieri, Chris Masson, Charles Miccinilli, Hugo Ramé, Elise Ren, Qiqi Rostamizadeh, Afshin Terrail, Jean Ogier du Toon, Anna-Monica Wang, Kan Xie, Stephan Xu, Zongzhe Zhukova, Viktoriya Asker, David Talwalkar, Ameet Abou-Amal, Othmane |
| author_facet | Cohen, Ben Khwaja, Emaad Doubli, Youssef Lemaachi, Salahidine Lettieri, Chris Masson, Charles Miccinilli, Hugo Ramé, Elise Ren, Qiqi Rostamizadeh, Afshin Terrail, Jean Ogier du Toon, Anna-Monica Wang, Kan Xie, Stephan Xu, Zongzhe Zhukova, Viktoriya Asker, David Talwalkar, Ameet Abou-Amal, Othmane |
| contents | We introduce Toto, a time series forecasting foundation model with 151 million parameters. Toto uses a modern decoder-only architecture coupled with architectural innovations designed to account for specific challenges found in multivariate observability time series data. Toto's pre-training corpus is a mixture of observability data, open datasets, and synthetic data, and is 4-10$\times$ larger than those of leading time series foundation models. Additionally, we introduce BOOM, a large-scale benchmark consisting of 350 million observations across 2,807 real-world time series. For both Toto and BOOM, we source observability data exclusively from Datadog's own telemetry and internal observability metrics. Extensive evaluations demonstrate that Toto achieves state-of-the-art performance on both BOOM and on established general purpose time series forecasting benchmarks. Toto's model weights, inference code, and evaluation scripts, as well as BOOM's data and evaluation code, are all available as open source under the Apache 2.0 License available at https://huggingface.co/Datadog/Toto-Open-Base-1.0 and https://github.com/DataDog/toto. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | This Time is Different: An Observability Perspective on Time Series Foundation Models Cohen, Ben Khwaja, Emaad Doubli, Youssef Lemaachi, Salahidine Lettieri, Chris Masson, Charles Miccinilli, Hugo Ramé, Elise Ren, Qiqi Rostamizadeh, Afshin Terrail, Jean Ogier du Toon, Anna-Monica Wang, Kan Xie, Stephan Xu, Zongzhe Zhukova, Viktoriya Asker, David Talwalkar, Ameet Abou-Amal, Othmane Machine Learning Artificial Intelligence We introduce Toto, a time series forecasting foundation model with 151 million parameters. Toto uses a modern decoder-only architecture coupled with architectural innovations designed to account for specific challenges found in multivariate observability time series data. Toto's pre-training corpus is a mixture of observability data, open datasets, and synthetic data, and is 4-10$\times$ larger than those of leading time series foundation models. Additionally, we introduce BOOM, a large-scale benchmark consisting of 350 million observations across 2,807 real-world time series. For both Toto and BOOM, we source observability data exclusively from Datadog's own telemetry and internal observability metrics. Extensive evaluations demonstrate that Toto achieves state-of-the-art performance on both BOOM and on established general purpose time series forecasting benchmarks. Toto's model weights, inference code, and evaluation scripts, as well as BOOM's data and evaluation code, are all available as open source under the Apache 2.0 License available at https://huggingface.co/Datadog/Toto-Open-Base-1.0 and https://github.com/DataDog/toto. |
| title | This Time is Different: An Observability Perspective on Time Series Foundation Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.14766 |