Core Viewpoint - The article discusses the development of ChatTS, a multimodal large language model (LLM) designed to support multivariate time series question answering and reasoning, addressing the limitations of existing models in handling time series data [1][6][14]. Group 1: Background and Motivation - The rapid advancement of multimodal LLMs has led to breakthroughs in various fields, but research on time series data integration remains limited [1][6]. - Existing attempts, such as TimeLLM, primarily focus on predictive tasks, failing to meet the complex understanding and reasoning needs in applications like AIOps and finance [1][6]. - There is a growing demand for LLMs that can handle time series data natively, enabling them to understand the shapes, fluctuations, and semantic meanings of time series [6][11]. Group 2: Challenges in Time Series Modeling - Traditional time series analysis methods often rely on statistical or AI models that require extensive task-specific training and structured input/output, lacking generalizability and interpretability [6][11]. - Current LLMs cannot directly process raw time series data, leading to limitations in existing approaches that convert time series into text or images [12][13]. - The scarcity of aligned time series and text data, along with the structural complexity of time series, poses significant challenges for model training and evaluation [11][12]. Group 3: ChatTS Development - ChatTS employs a "purely synthetic-driven" approach to overcome the lack of labeled data, creating an end-to-end data generation and model training framework [15]. - A detailed attribute system for time series is defined, ensuring the generated time series are diverse and accurately correspond to natural language descriptions [18]. - The model architecture is based on Qwen2.5-14B-Instruct, designed to natively perceive time series data by segmenting it into small patches and embedding it into the text context [22][23]. Group 4: Performance Evaluation - ChatTS has been evaluated using three datasets covering real-world and synthetic time series data, assessing alignment and reasoning tasks across 12 subcategories [31]. - In alignment tasks, ChatTS significantly outperformed baseline models, achieving F1 score improvements of 46% to 75% and over 80% accuracy in numerical tasks [32][33]. - For reasoning tasks, ChatTS demonstrated an average improvement of 25.8% over baseline models, showcasing its enhanced understanding capabilities [34]. Group 5: Future Potential - ChatTS represents a new paradigm in training multimodal models with synthetic data, indicating high potential for future applications in causal reasoning and root cause analysis [35].