This technical report describes the Time Series Optimized Transformer for Observability (Toto), a new state of the art foundation model for time series forecasting developed by Datadog. In addition to advancing the state of the art on generalized time series benchmarks in domains such as electricity and weather, this model is the first general-purpose time series forecasting foundation model to be specifically tuned for observability metrics. Toto was trained on a dataset of one trillion time series data points, the largest among all currently published time series foundation models. Alongside publicly available time series datasets, 75% of the data used to train Toto consists of fully anonymous numerical metric data points from the Datadog platform. In our experiments, Toto outperforms existing time series foundation models on observability data. It does this while also excelling at general-purpose forecasting tasks, achieving state-of-the-art zero-shot performance on multiple open benchmark datasets.

翻译：本技术报告介绍了面向可观测性的时序优化Transformer（Toto），这是由Datadog开发的一种用于时间序列预测的最新基础模型。该模型不仅在电力、气象等领域的通用时间序列基准测试中实现了技术突破，更是首个专门针对可观测性指标进行优化的通用时间序列预测基础模型。Toto基于一万亿时间序列数据点进行训练，其训练数据规模在所有已公开的时间序列基础模型中位居首位。除公开可用的时间序列数据集外，用于训练Toto的数据中有75%来自Datadog平台完全匿名的数值指标数据点。实验表明，Toto在可观测性数据上的表现优于现有时间序列基础模型。与此同时，该模型在通用预测任务中同样表现卓越，在多个开放基准数据集上实现了最先进的零样本预测性能。