Beyond Uniform Tokens: Adaptive Compression for Time Series Language Models

Problem
The paper addresses the inefficiency of uniform token processing in time series (TS) language models, which arises from the fundamentally different information structures of TS tokens and prompt tokens. Despite the advancements in large language models (LLMs) for TS analysis, existing methods do not exploit the uneven spectral contributions of TS tokens, leading to suboptimal performance. This work is a preprint and has not undergone peer review.

Method
The authors propose an adaptive token budgeting framework that leverages frequency-domain structures to compress TS tokens while progressively reducing the retention of prompt tokens across model layers. The framework is built on the observation that many TS tokens exhibit redundant frequency patterns, while a small subset retains critical temporal information. The model architecture is not explicitly detailed, but the approach involves a mechanism for dynamic token allocation based on the spectral analysis of the input data. The training compute requirements are not disclosed, but the framework is designed to enhance inference efficiency.

Results
The proposed method demonstrates significant improvements in inference speed and model performance across various tasks, including forecasting, classification, imputation, and anomaly detection. Specifically, the framework achieves up to 7.68× acceleration in inference time and shows performance gains in 78% of the evaluated settings compared to baseline models. The benchmarks used for evaluation are not specified, but the results indicate a substantial enhancement in the efficiency of TS language models.

Limitations
The authors acknowledge that their approach may not generalize to all types of time series data, particularly those with highly complex or irregular patterns. Additionally, the reliance on frequency-domain analysis may limit the applicability of the method to scenarios where such structures are not present. The paper does not address potential trade-offs between compression and model interpretability, nor does it explore the impact of varying the degree of token compression on model robustness.

Why it matters
This work has significant implications for the development of scalable TS foundation models, particularly in applications requiring real-time analysis and decision-making. By improving token efficiency, the proposed framework can facilitate the deployment of LLMs in resource-constrained environments, enhancing their usability in practical scenarios. The findings contribute to the ongoing discourse on optimizing model architectures for specific data types, as published in arXiv cs.CL. This research paves the way for future explorations into asymmetric token processing and its potential applications in other domains of machine learning.