arXiv:2510. 03519v3 Announce Type: replace-cross Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery.
STRIDE (Strategic Time-series Reasoning Injected via Distilled Embeddings) is a framework that embeds LLM reasoning directly into the continuous embedding space of Time Series Foundation Models (TSFMs). By distilling reasoning traces into a lightweight LLM and projecting its pooled hidden states as a continuous cross‑modal prior, STRIDE integrates semantic reasoning without fragmenting numerical values or inflating sequence lengths. The method is jointly optimized with cross‑entropy and quantile losses, can be attached to various TSFM architectures, and achieves top performance on the GIFT‑Eval leaderboard and strong results on TFRBench, with ablations showing that the gains come from strategic reasoning integration.
arXiv:2608. 05238v1 Announce Type: new Abstract: Training multimodal models to align time series with language runs into a self-supervision trap.
arXiv:2506. 10630v3 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures.
arXiv:2608. 01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.
arXiv:2606. 12481v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis.
arXiv:2609.16055v1 Announce Type: cross Abstract: Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasonin...
arXiv:2506.10630v4 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techni...
TimeBraid is a family of unified models that combine pretrained language models with pretrained time‑series foundation models using interleaved global residual attention layers. The models inherit instruction following, reasoning, and continuous‑signal perception, fusing both modalities into a shared representation space for understanding and generation. The design focuses on aligning representation spaces, grounding language in temporal structure, balancing understanding with generation, and maintaining stable joint optimization, supported by 2.2 M curated series‑text pairs and 4.9 M instruction‑tuning samples. Across diverse benchmarks, TimeBraid competes with larger general‑purpose and task‑specific models.
Lapras is a post‑training framework for Time Series Language Models that replaces explicit Chain‑of‑Thought reasoning with continuous latent thoughts in a joint time‑series‑language space. By self‑distilling from a teacher that generates textual rationales, the student model aligns its hidden states at the answer stage, transferring reasoning ability while producing only the final answer in text. Experiments on four TSLM backbones and five benchmarks show Lapras boosts average F1 by up to 10.79% over explicit CoT and reduces token generation by 23.9×, while still enabling readable reasoning traces through standard decoding.
The paper introduces a dual‑view approach for time‑series language models, combining line plots that preserve absolute measurements with normalized delay embeddings that highlight temporal structure. By fusing these complementary visual representations through dedicated encoders and multi‑level feature fusion, the model achieves significant performance gains on several benchmarks, outperforming single‑view baselines and existing TSLMs. The results demonstrate that preserving both measurement frame and temporal structure is an effective design principle for general‑purpose time‑series reasoning.
arXiv:2608. 10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples.