New Framework Helps Language Models Forecast Time Series by Bridging Data Modality Gap

Large language models have shown surprising capability in time series forecasting, yet they struggle with fundamental limitations. A new research framework called TALON addresses two key barriers that have constrained their effectiveness: the variability of temporal patterns across datasets and the mismatch between how numbers and language are represented.

The Core Problem#

Time series forecasting—predicting future values based on historical trends—requires models to recognize patterns that shift and vary in character. Meanwhile, large language models operate on discrete language tokens, not continuous numerical sequences. This creates what researchers call a "modality gap": the way these systems naturally process information doesn't align well with how numerical time series data is structured.

How TALON Works#

The framework introduces two main components. A Heterogeneous Temporal Encoder segments multivariate time series into meaningful blocks, then applies specialized "experts" to each segment to capture distinct temporal behaviors. Separate sections of a dataset might follow different patterns—a model learns to handle each appropriately.

The Representation Alignment Module translates temporal features into forms that language models can work with effectively. This approach removes the need for carefully hand-written prompts at inference time, making the system more flexible and practical.

Results and Impact#

Testing across seven real-world datasets showed TALON achieved forecast improvements of up to 11% in mean squared error compared to recent competing methods, while maintaining computational efficiency. The findings suggest that combining pattern-aware modeling with representation-level alignment is a promising direction for deploying language models beyond their traditional text domain.