TempoGPT: making LLMs smarter with time series data via quantization.
TempoGPT enhances time series reasoning by quantizing temporal embeddings into discrete tokens, enabling consistent representation with text.
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https://arxiv.org/abs/2501.07335
Original Problem 🤔:
→ Multi-modal LLMs struggle with complex reasoning in time series data.
→ Time series labels often lack detailed reasoning or analysis.
→ Inconsistent representation of temporal and textual data hinders multi-modal alignment.
Solution in this Paper 💡:
→ TempoGPT uses a novel multi-modal data construction approach within a white-box system, allowing systematic analysis of variable-system relationships.
→ TempoGPT quantizes temporal embeddings into discrete tokens using a predefined codebook.
→ A shared embedding layer processes both temporal and textual tokens, ensuring consistent representation.
Key Insights from this Paper 🔑:
→ Quantizing temporal embeddings improves multi-modal alignment.
→ Constructing data with explicit reasoning processes improves reasoning capabilities.
→ Consistent representation of temporal and textual information is crucial for complex time series reasoning.
Results 📊:
→ TempoGPT achieves state-of-the-art performance (83.3% average conclusion accuracy) in complex time series reasoning tasks.
→ Outperforms continuous embedding-based methods by a large margin, sometimes exceeding 100% improvement in specific tasks after quantization.
→ Shows superior logical reasoning accuracy (69.3%) and lower deception rate (2.7%) compared to baselines.
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