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TimeGPT: Machine Learning for Time Series, Made Accessible

Max Mergenthaler and Azul Garza Ramirez on TimeGPT, a frontier model for time series applications.

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Max Mergenthaler (CEO) and Azul Garza Ramirez (CTO) are co-founders of Nixtla, a startup that seeks to make cutting-edge predictive insights widely accessible. They build tools that  eliminate the necessity for organizations to maintain specialized teams of machine learning engineers, thereby streamlining the integration of advanced predictive technologies into various business operations. In this episode we discuss TimeGPT, Nixtla’s new frontier model for time series forecasting. TimeGPT can accurately predict future values for novel time series, using only historical data points. This promising “zero-shot” capability eliminates time-consuming retraining.

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Interview highlights – key sections from the video version:

  1. Target users for TimeGPT and how to use TimeGPT
  2. Formatting data for TimeGPT
  3. High-level overview of the training data for TimeGPT
  4. Evaluation and benchmarking
  5. Latency and inference speed
  6. Domains and areas where TimeGPT has been successfully used
  7. Suitable for forecasting and anomaly detection
  8. Fine tuning TimeGPT
  9. Proprietary nature and other early feedback
  10. Potential impact of TimeGPT on the forecasting field
  11. Timelines for TimeGPT-2 and the size of TimeGPT
  12. Mixture of experts and retrieval augmented generation
  13. More on the training data used for TimeGPT
  14. Suitability for real-time applications
  15. Automation and the future of time series analysis

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