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:2510. 03519v3 Announce Type: replace-cross Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery.
arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for qu...
arXiv:2602. 23161v4 Announce Type: replace Abstract: Time series reasoning demands both the perception of complex dynamics and logical depth.
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.
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.
arXiv:2607. 25947v1 Announce Type: new Abstract: Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications.
CodeTS introduces a verifiable framework for generating time series from natural language by translating textual temporal descriptions into executable code, which then produces the desired series. The approach constructs aligned Text‑Code‑TS triplets for supervised initialization and employs multi‑stage execution‑based rewards to ensure code validity and time‑series quality. Experiments on eight benchmarks show that CodeTS outperforms both LLM‑based and supervised generative baselines, offering a strong zero‑shot solution for Text‑to‑TS generation.
TSQueryBench is a synthetic benchmark comprising 500 time‑series instances and 10 query types, each paired with correct, partially correct, and incorrect natural‑language explanations. The study evaluates six large language models on explanation generation, ranking, scoring, and anomaly detection, revealing that models often fail to generate numerically correct explanations yet can reliably identify or score correct ones. These findings suggest that rubric‑guided LLM evaluation is more dependable than generation for numerically grounded time‑series reasoning.
arXiv:2607. 08940v1 Announce Type: new Abstract: Time series reasoning is essential for real-world problem-solving.
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...
arXiv:2512. 14332v2 Announce Type: replace-cross Abstract: The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately.
arXiv:2606. 18986v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering.