About this episode
This week, we’ve covering Amazon’s time series model: Chronos. Developing accurate machine-learning-based forecasting models has traditionally required substantial dataset-specific tuning and model customization. Chronos however, is built on a language model architecture and trained with billions of tokenized time series observations, enabling it to provide accurate zero-shot forecasts matching or exceeding purpose-built models.
We dive into time series forecasting, some recent research our team has done, and take a community pulse on what people think of Chronos.
Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.
Get every episode summarized
Each time Deep Papers publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from Deep Papers
CUGA Agent: From Benchmarks to Business Impact of IBM's Generalist Agent
Deep Papers
TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture
Deep Papers
Meta AI Researcher Explains ARE and Gaia2: Scaling Up Agent Environments and Eva...
Deep Papers
Georgia Tech's Santosh Vempala Explains Why Language Models Hallucinate, His Res...
Deep Papers