Chronos: Learning the Language of Time Series with Abdul Fatir Ansari
EPISODE 685
|
MAY
20,
2024
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About this Episode
Today we're joined by Abdul Fatir Ansari, a machine learning scientist at AWS AI Labs in Berlin, to discuss his paper, "Chronos: Learning the Language of Time Series." Fatir explains the challenges of leveraging pre-trained language models for time series forecasting. We explore the advantages of Chronos over statistical models, as well as its promising results in zero-shot forecasting benchmarks. Finally, we address critiques of Chronos, the ongoing research to improve synthetic data quality, and the potential for integrating Chronos into production systems.
About the Guest
Abdul Fatir Ansari
AWS AI Labs
Resources
- Papers:
- Chronos: Learning the Language of Time Series
- Large Language Models Are Zero-Shot Time Series Forecasters
- Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
- LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
- Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
- Unified Training of Universal Time Series Forecasting Transformers (Moirai)
