Toto: Time Series Optimized Transformer for Observability
This work addresses forecasting challenges for observability metrics in domains like IT monitoring, representing an incremental advance with domain-specific tuning.
The paper tackles time series forecasting by introducing Toto, a foundation model specifically tuned for observability metrics, which achieves state-of-the-art zero-shot performance on multiple open benchmarks and outperforms existing models on observability data.
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.