ChatTS-14B – ByteDance’s Open-Source Large Model for Time Series Understanding and Reasoning

What is ChatTS-14B?

ChatTS-14B is an open-source large language model developed by ByteDance’s research team, specifically designed for time series understanding and reasoning. With 14 billion parameters, the model is fine-tuned from Qwen2.5-14B-Instruct and leverages synthetic data alignment techniques to significantly enhance its performance on time series tasks. ChatTS-14B supports natural language interaction, allowing users to analyze, forecast, and reason about time series data using simple instructions. Example use cases include financial market trend analysis, weather forecasting, and industrial process optimization. The model is released under the Apache 2.0 license, with access to model weights, documentation, and source code for flexible use and secondary development.

Key Features of ChatTS-14B

• Time Series Understanding and Reasoning: ChatTS-14B provides in-depth analysis and reasoning capabilities for time series data, helping users identify trends, patterns, and anomalies.

• Natural Language Interaction: Users can interact with the model through natural language, inputting time series data and posing questions or instructions. The model responds with insights in natural language.

Technical Principles of ChatTS-14B

• Model Architecture: ChatTS-14B is fine-tuned from the Qwen2.5-14B-Instruct model—a 48-layer Transformer architecture with 14 billion parameters. This structure enables the model to handle large-scale input and capture complex patterns in time series data using multi-head self-attention.

• Synthetic Data Alignment: To improve performance on time series tasks, ChatTS-14B uses synthetic data alignment. By generating synthetic time series and aligning them with real-world data, the model learns time-based features more effectively, excelling in reasoning tasks.

• Fine-Tuning Techniques: Building on pretraining, ChatTS-14B is fine-tuned specifically for time series applications. It learns from a large corpus of time series samples, adjusting internal parameters to better handle time series analysis and reasoning.

Project Links for ChatTS-14B

• GitHub Repository: https://github.com/NetManAIOps/ChatTS

• Hugging Face Model Hub: https://huggingface.co/bytedance-research/ChatTS-14B

• arXiv Technical Paper: https://arxiv.org/pdf/2412.03104

Application Scenarios of ChatTS-14B

• Financial Market Analysis: ChatTS-14B can process financial time series such as stock prices and trading volumes, aiding investors in trend analysis, risk assessment, and anomaly detection.

• Weather Forecasting: The model analyzes meteorological data such as temperature, humidity, and wind speed to provide forecasts and early warnings. Users can interact with it in natural language for weather trend analysis and advice.

• Industrial Process Optimization: ChatTS-14B monitors production equipment conditions (e.g., temperature, pressure, vibration), predicts failures in advance, and optimizes workflows, helping to improve efficiency and reduce maintenance costs.

• Healthcare and Medical Monitoring: In healthcare, the model analyzes vital signs like heart rate, blood pressure, and blood sugar, assisting doctors with patient monitoring and diagnosis. It can also interpret time series data like ECGs and provide diagnostic suggestions.

• AIOps (Artificial Intelligence for IT Operations): ChatTS-14B analyzes system metrics such as CPU usage, memory consumption, and network latency to quickly locate root causes of issues and offer diagnostic advice. With natural language interfaces, it helps operations teams troubleshoot more efficiently.