[VLDB’ 25] ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning ChatTS focuses on Understanding and Reasoning about time series, much like what vision/video/audio-MLLMs do. This repo provides code, datasets and model for ChatTS: ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning. ChatTS is a Multimodal LLM built natively for time series as a core modality： — ✅ Native support for multivariate time series — ✅ Flexible input: Supports multivariate time series with different lengths and flexible dimensionality — ✅ Conversational understanding + reasoning: Enables interactive dialogue over time series to explore insights about time series — ✅ Preserves raw numerical values: Can answer statistical questions, such as «How large is the spike at timestamp t?» Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data: !Chat — This model is fine-tuned on the QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) model. For more…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: xiezhe24
Теги: qwen3ts, feature-extraction, conversational, custom_code, zh, en, compressed-tensors
Лайков: 3 | Загрузок: 28
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
