Representing Time Series as Structured Programs for LLM Reasoning
📰 ArXiv cs.AI
Learn to represent time series as structured programs for LLM reasoning to improve time-series analysis capabilities
Action Steps
- Represent time series data as structured programs using programming languages like Python
- Convert numerical sequences into textual representations using techniques like serialization or encoding
- Fine-tune pre-trained LLMs on time-series data to adapt to the new representation
- Use the fine-tuned LLM to reason about the time series data and make predictions
- Evaluate the performance of the LLM on time-series analysis tasks using metrics like accuracy and mean squared error
Who Needs to Know This
Data scientists and AI engineers can benefit from this approach to leverage LLMs for time-series analysis, enabling more accurate predictions and insights
Key Insight
💡 Representing time series as structured programs enables LLMs to reason about them effectively, improving time-series analysis capabilities
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📊 Represent time series as structured programs for LLM reasoning to unlock new insights in time-series analysis #LLMs #TimeSeriesAnalysis
Key Takeaways
Learn to represent time series as structured programs for LLM reasoning to improve time-series analysis capabilities
Full Article
Title: Representing Time Series as Structured Programs for LLM Reasoning
Abstract:
arXiv:2606.12481v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis. However, time series lie outside their native textual modality, raising a fundamental question: how should time series be represented so that LLMs can reason about them effectively? Existing work typically serializes raw numerical sequences or fine-tunes pre-trained LLMs on time-s
Abstract:
arXiv:2606.12481v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis. However, time series lie outside their native textual modality, raising a fundamental question: how should time series be represented so that LLMs can reason about them effectively? Existing work typically serializes raw numerical sequences or fine-tunes pre-trained LLMs on time-s
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