Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment
Yanru Sun, Emadeldeen Eldele, Zongxia Xie, Yucheng Wang, Wenzhe Niu, Qinghua Hu, Chee Keong Kwoh, Min Wu
TL;DR
This paper introduces TALON, a framework that adapts large language models for time series forecasting by modeling temporal heterogeneity and aligning semantic representations, leading to improved accuracy on real-world benchmarks.
Contribution
The paper presents a novel approach combining a heterogeneous temporal encoder and semantic alignment to effectively adapt LLMs for time series forecasting.
Findings
Achieves up to 11% MSE improvement over state-of-the-art methods.
Effectively models diverse temporal patterns with localized experts.
Enables integration of time series into language models without handcrafted prompts.
Abstract
Large Language Models (LLMs) have recently demonstrated impressive capabilities in natural language processing due to their strong generalization and sequence modeling capabilities. However, their direct application to time series forecasting remains challenging due to two fundamental issues: the inherent heterogeneity of temporal patterns and the modality gap between continuous numerical signals and discrete language representations. In this work, we propose TALON, a unified framework that enhances LLM-based forecasting by modeling temporal heterogeneity and enforcing semantic alignment. Specifically, we design a Heterogeneous Temporal Encoder that partitions multivariate time series into structurally coherent segments, enabling localized expert modeling across diverse temporal patterns. To bridge the modality gap, we introduce a Semantic Alignment Module that aligns temporal features…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
1. I agree that there is currently a significant gap between LLMs and time series forecasting. Existing LLM-based methods—including reprogramming approaches such as Time-LLM and prompt-based approaches such as LLMTime—indeed have certain limitations. The paper identifies two reasonable underlying causes, namely temporal heterogeneity and modality differences, which are accurately and clearly articulated in the introduction. 2. The proposed method adopts a training–inference decoupled design, fun
1. (major) Choice of mode quantization features: The MoE-like architectural design of this paper is impressive. However, the selection of time series statistical features—trend strength, local variation, and autocorrelation—appears somewhat simplistic. These statistics may not be sufficient to uniquely or accurately characterize the properties of a time series patch. It remains unclear whether incorporating additional or more informative statistical features could provide a more precise represen
1. Comprehensive Temporal Modeling: By combining heterogeneous experts in the HTE module, TALON effectively captures diverse temporal behaviors including trends, local fluctuations, and long-range dependencies. This multi-expert design enhances its ability to adapt to nonstationary and complex temporal patterns. 2. Semantic–Numerical Integration: The Semantic Alignment Module (SAM) bridges numerical time-series representations with linguistic semantics through contrastive learning. This integra
1. Information Loss in Cross-Modal Alignment: During cross-modal contrastive alignment, temporal embeddings may suffer from information loss as the optimization drives them excessively close to the semantic embeddings. Consequently, the model’s final representations could become dominated by textual prompts, diminishing the independent contribution of the HTE module and weakening the preservation of intrinsic temporal dynamics. The paper lacks diagnostic experiments or regularization strategies
1. Clear research motivation and structural design: The paper clearly articulates the structural and modal differences between time series tasks and language modeling, systematically addressing this issue through the HTE and SAM modules. The framework exhibits logical coherence with focused innovations. 2. Rational and interpretable module design: The HTE module employs local statistical features for expert dynamic routing, integrating linear, CNN, and LSTM experts to capture multi-scale tempor
1. Lack of Innovation and Differentiation: Although TALON conceptually integrates heterogeneous modeling with semantic alignment, its implementation primarily relies on existing MoE routing mechanisms and contrastive learning frameworks. The three features—trend, fluctuation, and autocorrelation—in the HTE module are relatively conventional, lacking exploration of more complex patterns such as frequency domain and long-range dependencies. We recommend the authors further elaborate on the theoret
1. Prompt-free inference with token-level alignment: Avoids handcrafted prompts and reduces input redundancy while semantically grounding features via contrastive alignment. 2. The architecture HTE explicitly models heterogeneity with adaptive routing and complementary experts, supported by balanced-load regularization and strong ablations. 3. TALON has competitive accuracy with a compact head and frozen LLM backbone (~1.7M params; fast inference).
1. Some baseline results, such as TIME-LLM, are lower than those reported in the original paper, and TALON shows no clear advantage compared to the original results. Certain related works or baselines, such as TEMPO [1] and [2], are also missing. 2. Token-adaptive prompts are not used during inference; the paper could further explore whether reintroducing lightweight textual cues would help in out-of-distribution settings. Context(relevant to time series)-aided time series forecasting would make
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Taxonomy
TopicsTime Series Analysis and Forecasting · Forecasting Techniques and Applications · Machine Learning in Healthcare