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.
Azul (she/her) is a highly experienced ML engineer, with a background in economics and mathematics. She is CTO and co-founder of Nixtla. With over a decade of expertise in deploying ML models in production for large financial institutions, Azul has a proven track record of delivering end to end products. She is passionate about creating usable, scalable, and open-source ML products, and is a co-maintainer of several popular Python libraries. Azul's expertise in the field has also earned her recognition as a speaker at multiple Pycons and author of peer-reviewed papers.
Max is the CEO and Co-Founder of Nixtla, a time-series research and deployment startup. He is also a seasoned entrepreneur with a proven track record as the founder of multiple technology startups. With a decade of experience in the ML industry, he has extensive expertise in building and leading international data teams. Max has also made notable contributions to the Data Science field through his co-authorship of papers on forecasting algorithms and decision theory. In addition, he is a co-maintainer of several open-source libraries in the Python ecosystem. Max's passion lies at the intersection of business and technology.
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