🧠 Interpretable Prototype: A Bridge from Text to Time-Series for LLM

This repository presents the code and resources for the research project "Interpretable Prototype: A Bridge from Text to Time-Series for LLM"

📌 Overview

This project explores how Large Language Models (LLMs) can be leveraged for time-series analysis tasks, including forecasting, classification, and anomaly detection.
To overcome the modality gap between time-series data and natural language, we introduce a novel method that aligns time-series embeddings with interpretable text prototypes.

🚀 Motivation

• Generalizability: Traditional time-series models often require task-specific architectures and domain expertise.
• LLM Potential: LLMs show strong few-shot/zero-shot capabilities, but cannot directly handle raw time-series due to modality mismatch.
• Solution: Use interpretable text prototypes to align time-series embeddings with the LLM’s text embedding space.

🧠 Main Idea

• Use text prototypes (e.g., "Trend", "Volatility", "Consumption") to represent key characteristics of time-series data.
• Train a model to align time-series tokens with these interpretable text embeddings.
• Improve interpretability, alignment, and performance on downstream tasks.

🔍 Methodology

📌 Framework

1. Text Prototype Selection

   • General: Trend, Seasonality, Cyclicality, Volatility, ...
   • Dataset-specific: Electricity, Usage, Household, ...

2. Embedding Training

   • Use CKA (Centered Kernel Alignment) Loss to align time-series embeddings with text embeddings.
   • Fixed text-prototypes + learnable weights for interpretability and training efficiency.

3. Downstream Tasks

   • Apply to forecasting and classification using aligned embeddings.

4. Interpretability

   • Visualize attention maps and cosine similarity between time steps and prototypes.

🔧 Architecture Highlights

• Pre-trained LLM backbone (Transformer blocks)
• Time-series embedder
• Text-prototype alignment module
• Cosine similarity + attention analysis

📊 Dataset

ElectricDevices Dataset

• Behavioral energy usage data from UK households
• 251 households, sampled every 2 minutes over 24 hours
• 720 time steps per sequence

📁 Project Structure

📁 prototype-alignment-LLM/

  ├── test
    ├── ElectricDevices/                # Time-series datasets and labels
    ├── code/                           # Model architecture and training scripts
    ├── visualization/                  # Visualizations of Embedding
    ├── visulization_semantic/          # Visualizations of Embedding in Semantic Space matched with Words
  ├──README.md

📈 Key Metrics

• CKA score improvements before & after alignment
• Prototype attention maps
• Interpretability with domain-relevant text concepts

🧾 References

• OneFitsAll: Pretrained LM for Time-Series (NeurIPS 2023)
• Time-LLM (ICLR 2024)
• CALF (arXiv 2024)
• TEST (ICLR 2024)
• X-VILA (arXiv 2024)

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🧑‍💻 Authors

• Jiyun Kim
  Department of Computer Science & Engineering
  Korea University
  DAIS Lab (Lab Meeting 11.29)

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