TimeGPT: A Foundation Large Time Series Model

Time series—data ordered chronologically—constitutes the underlying fabric of systems, enterprises, and institutions. Its impact spans from measuring ocean tides to tracking the daily closing value of the Dow Jones. This type of data representation is indispensable in sectors such as finance, healthcare, meteorology and social sciences.

However, the current theoretical and practical understanding of time series hasn't yet achieved a level of consensus among practitioners that mirrors the widespread acclaim for generative models in other fundamental domains of the human condition, like language and perception. Our field is still divided and highly specialized. Efforts in forecasting science have fallen short of fulfilling the promises of genuinely universal pre-trained models.

In this talk, we will and introduce TimeGPT, the first pre-trained foundation model for time series forecasting that can produce accurate predictions across a diverse array of domains and applications without additional training. A general pre-trained model constitutes a groundbreaking innovation that opens the path to a new paradigm for the forecasting practice that is more accessible and accurate, less time-consuming, and drastically reduces computational complexity.

During the talk, we will guide practitioners to leverage TimeGPT and the distributed capabilities from Ray to perform forecasting and anomaly detection at scale.