In a significant leap for artificial intelligence, ByteDance and Tsinghua University have unveiled ChatTS, a cutting-edge temporal multimodal large model (MLLM). This collaboration marks a milestone in AI's ability to process and reason with time-series data—a capability that has long eluded conventional models.

Led by Zhe Xie, a Ph.D. candidate at Tsinghua, the research team combined academic rigor with industry expertise. ByteDance scientists Li Zeyan and He Xiao contributed alongside faculty advisors Tieying Zhang (ByteDance) and Dan Pei (Tsinghua). Their creation solves a persistent challenge: traditional statistical models require extensive training data and complex preprocessing, limiting their flexibility.

Why does this matter? Temporal data powers everything from stock market predictions to server performance monitoring. Yet until now, AI systems struggled with natural language understanding of time-series patterns. ChatTS changes the game by natively supporting multi-variable temporal reasoning—imagine asking an AI "What caused this unusual server spike last Tuesday?" and getting an insightful answer.

The team pioneered a pure synthetic-driven approach, building an end-to-end framework that generates realistic time-series data paired with accurate natural language descriptions. This breakthrough allows ChatTS to:

• Analyze complex multi-variable patterns
• Identify never-before-seen fluctuations
• Automatically name detected anomalies

In practical tests, the model demonstrated remarkable precision. It extracts abnormal fluctuations without explicit prompts—a capability that could transform fields like financial fraud detection or industrial equipment monitoring.

Industry experts anticipate widespread impact. "This isn't just another language model," observes one database researcher. "ChatTS bridges the gap between raw temporal data and actionable insights." The technology's potential extends to:

• Real-time financial market analysis
• Predictive maintenance in manufacturing
• Automated IT incident diagnosis

The research earned recognition at VLDB2025, one of computer science's top conferences. As enterprises increasingly rely on time-sensitive data, ChatTS positions itself as an essential tool for the algorithmic age.

Key Points

1. ChatTS introduces native support for temporal question-answering, overcoming previous AI limitations
2. The model uses synthetic data generation to ensure accurate language-time series alignment
3. Demonstrated capabilities include anomaly detection without explicit training
4. Potential applications span finance, IT operations, and industrial analytics
5. Research accepted by prestigious VLDB2025 conference