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" - arxiv.org/abs/2403.07815. 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.
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📖 CHAPTERS
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00:00 - Introduction
02:11 - Inspiration for Chronos
04:30 - Overview of statistical models
07:04 - Overfitting
08:17 - LLMs in time series forecasting
10:20 - Tokenization
15:25 - Why T5?
16:35 - Data augmentation
25:28 - Evaluation
27:45 - Result
31:15 - In domain vs zero shot
33:35 - Performance across different patterns
36:25 - Critique of Chronos
40:15 - Chronos in production
41:00 - Future of Chronos
42:00 - Conclusion
🔗 LINKS & RESOURCES
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Large Language Models Are Zero-Shot Time Series Forecasters - arxiv.org/pdf/2310.07820
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models - arxiv.org/abs/2310.01728
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters - arxiv.org/pdf/2308.08469
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
- arxiv.org/abs/2310.08278
Unified Training of Universal Time Series Forecasting Transformers (Moirai) - arxiv.org/abs/2402.02592
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