The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Chronos: Learning the Language of Time Series with Abdul Fatir Ansari - #685
May 20, 2024
In this discussion, machine learning scientist Abdul Fatir Ansari from AWS AI Labs dives into his groundbreaking work, Chronos, which applies language models to time series forecasting. He reveals the competitive edge Chronos has over traditional statistical methods and its surprising success in zero-shot forecasting. The conversation also touches on practical challenges like data augmentation and evaluation setups, as well as ongoing efforts to enhance synthetic data quality. Ansari sheds light on the promising future for integrating Chronos into real-world applications.
43:05
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Quick takeaways
- Kronos addresses the challenge of improving benchmark datasets by highlighting the limitations of using simple models in production time series systems.
- Shift towards leveraging language models like T5 for time series forecasting is observed, with Kronos demonstrating promising zero-shot forecasting results.
Deep dives
Kronos' Approach to Time Series Forecasting and Dataset Challenges
Kronos addresses the issue of improvements on benchmark datasets lacking real absolute performance enhancement, particularly due to the common use of the same datasets in the literature. This highlights the challenges in performance evaluation. One major inspiration for Kronos was observed limitations in using simple models in production time series systems, as setting up new state-of-the-art models is comprehensive and time-consuming. This leads to the prevalent usage of simplistic models like seasonal lag, which performs adequately but underutilizes advanced deep learning models.
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