Beyond Uniform Tokens: Adaptive Compression for Time Series Language Models

2026-06-11 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new adaptive token budgeting framework addresses the inefficiency of large language models (LLMs) in processing time series (TS) and textual prompt tokens uniformly. Research shows that TS tokens exhibit highly uneven spectral contributions, with many sharing redundant frequency patterns while a small subset preserves critical temporal evidence. Additionally, prompt-token influence attenuates with model depth, indicating that full prompt retention across all layers is unnecessary. The proposed framework compresses TS tokens via frequency-domain structure and progressively reduces prompt tokens across layers. This approach demonstrates significant improvements, achieving up to 7.68x inference acceleration and performance gains in 78% of evaluated settings, including forecasting, classification, imputation, and anomaly detection, highlighting the effectiveness of asymmetric token compression for scalable TS foundation models.

Key takeaway

For Machine Learning Engineers optimizing large language models for time series analysis, this adaptive token budgeting framework offers a critical path to efficiency. You should consider implementing asymmetric token compression, which leverages the distinct information structures of TS and prompt tokens. This approach can significantly accelerate inference by up to 7.68x and improve performance in diverse tasks like forecasting and anomaly detection, making your TS foundation models more scalable.

Key insights

Adaptive token compression for time series LLMs improves efficiency and performance by leveraging asymmetric information structures.

Principles

• TS tokens have highly uneven spectral contributions.
• Prompt-token influence attenuates with model depth.

Method

Compress TS tokens via frequency-domain structure and progressively reduce prompt tokens across LLM layers based on their diminishing influence.

In practice

• Apply to time series forecasting tasks.
• Enhance LLM performance in anomaly detection.

Topics

• Large Language Models
• Time Series Analysis
• Token Compression
• Adaptive Token Budgeting
• Frequency Domain Processing
• Inference Acceleration

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.