Revolutionizing Time Series Analysis with LLMs
A training-free approach using tabular representations
TableTime introduces a novel framework that reformulates time series classification as a table understanding task, leveraging the inherent capabilities of Large Language Models without additional training.
- Transforms complex time series data into tabular representations that LLMs can naturally process
- Achieves state-of-the-art performance on multivariate time series classification benchmarks
- Provides training-free implementation that eliminates the need for fine-tuning or embedding alignment
- Demonstrates robust generalization across diverse datasets with minimal prompting
This research has significant implications for medical applications including ECG/EEG analysis, patient monitoring, and diagnostic systems where time series data is abundant but labeled data may be scarce.