Table of Links Background
Problem statement
Model architecture
Training data
Results
Conclusions
Impact statement
Future directions
Contributions
Acknowledgements and References Background Background Problem statement Problem statement Model architecture Model architecture Training data Training data Results Results Conclusions Conclusions Impact statement Impact statement Future directions Future directions Contributions Contributions Acknowledgements and References Acknowledgements and References Appendix Appendix 6 Conclusions Toto, through a novel architecture and pre-training corpus, demonstrates state-of-the-art performance both on public benchmarks and on the Datadog observability benchmark. We look forward to sharing many more technical details, experiments, and benchmark results in a forthcoming paper. 7 Impact statement In developing Toto, Datadog follows a structured approach to ensure responsible development, focusing on identifying, assessing, and mitigating potential risks associated with the use of our model. Given that Toto is not intended for mass distribution and specifically generates time series forecasts for observability data, the potential harms are considerably lower compared to more general-purpose models. At Datadog, our primary focus is on ensuring the accuracy, reliability, and security of the forecasts generated by Toto, which are crucial for maintaining and optimizing infrastructure and application performance. We carefully analyze the potential for Toto to produce incorrect or misleading forecasts that could impact decision-making processes in critical systems. Additionally, we consider the implications of Toto's performance across diverse datasets, ensuring it can generalize well without introducing significant errors. 8 Future directions Many exciting areas of exploration remain for further study. If you are interested in working with us, please reach out to the authors by email. Some future research questions that particularly intrigue us include: • Multi-modal inputs: Incorporate additional input modalities such as query metadata and captions to enhance forecast performance. • Multi-modal inputs • Autonomous troubleshooting agents: Augment Datadog's AI agents [50] for troubleshooting and incident response by integrating modality-specific foundation models like Toto to improve their reasoning and planning capabilities with telemetry data. • Autonomous troubleshooting agents • Conversational interfaces: Align time series forecasting models with LLMs to develop conversational agents capable of interpreting and reasoning about time series data. • Conversational interfaces • Model enhancements and scaling: Explore ways to improve and scale model performance through optimizations such as new types of input embeddings, attention mechanisms, and examining alternative variate groupings to capture richer interactions. • Model enhancements and scaling 9 Contributions Contributors are listed in alphabetical order. 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Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/ forum?id=AyajSjTAzmg. [47] Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. In Proc. 39th International Conference on Machine Learning (ICML 2022), 2022. [48] J. Scott Armstrong. Long-range Forecasting: From Crystal Ball to Computer. John Wiley & Sons, New York, 1985. ISBN 9780471822608. [49] R. J Hyndman and A. B. Koehler. Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 2006. [50] Datadog. Bits ai: Reimagining the way you run operations with autonomous investigations, 2024. URL https://www.datadoghq.com/ blog/bits-ai-autonomous-investigations. Appendix A.1 Model architecture A.1 Model architecture A.2 Results A.2 Results Authors:
(1) Ben Cohen (ben.cohen@datadoghq.com);
(2) Emaad Khwaja (emaad@datadoghq.com);
(3) Kan Wang (kan.wang@datadoghq.com);
(4) Charles Masson (charles.masson@datadoghq.com);
(5) Elise Rame (elise.rame@datadoghq.com);
(6) Youssef Doubli (youssef.doubli@datadoghq.com);
(7) Othmane Abou-Amal (othmane@datadoghq.com). Authors: Authors: (1) Ben Cohen (ben.cohen@datadoghq.com); (2) Emaad Khwaja (emaad@datadoghq.com); (3) Kan Wang (kan.wang@datadoghq.com); (4) Charles Masson (charles.masson@datadoghq.com); (5) Elise Rame (elise.rame@datadoghq.com); (6) Youssef Doubli (youssef.doubli@datadoghq.com); (7) Othmane Abou-Amal (othmane@datadoghq.com). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

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Toto: Time Series Optimized Transformer for Observability

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