ChatTS-14B - ByteDance's open-source large-scale model for time series understanding and inference

What is ChatTS-14B?

ChatTS-14B is a large-scale language model focused on time series understanding and reasoning, open-sourced by ByteDance's research team, with 14 billion parameters. It is finely tuned from Qwen2.5-14B-Instruct and significantly improves its performance in time series tasks through synthetic data alignment techniques. The model supports natural language interaction, allowing users to perform analysis, prediction, and reasoning on time series data through simple commands, such as financial market trend analysis, weather forecasting, or industrial process optimization. ChatTS-14B is open-sourced under the Apache 2.0 license, providing model weights, usage documentation, and a code library for developers to freely use and further develop.

Main functions of ChatTS-14B

• Time series understanding and reasoningChatTS-14B can perform in-depth analysis and reasoning on time series data, helping users understand trends, patterns, and changes in the data.
• Natural Language InteractionUsers can interact with the model using natural language, inputting time series data and asking questions or giving instructions. The model will then return the analysis results in natural language.

Technical Principles of ChatTS-14B

• Model ArchitectureChatTS-14B is a fine-tuned version of the Qwen2.5-14B-Instruct model. Qwen2.5-14B-Instruct is a 48-layer Transformer model with 14 billion parameters. This architecture can handle large-scale input data and captures complex patterns in time series data through a multi-head self-attention mechanism.
• Synthetic data alignment techniquesTo improve the model's performance in time series tasks, ChatTS-14B employs synthetic data alignment technology. By generating synthetic time series data and aligning it with real data, the model can better learn the characteristics and patterns of time series, resulting in excellent performance in inference tasks.
• Fine-tuning technologyChatTS-14B is fine-tuned for time series tasks based on pre-training. During the fine-tuning process, the model learns from a large number of time series samples and adjusts its internal parameters to better adapt to time series analysis and inference tasks.

ChatTS-14B project address

• Github repository:https://github.com/NetManAIOps/ChatTS
• HuggingFace model library:https://huggingface.co/bytedance-research/ChatTS-14B
• arXiv technical paper:https://arxiv.org/pdf/2412.03104

Application scenarios of ChatTS-14B

• Financial Market AnalysisChatTS-14B can process financial time-series data such as stock prices and trading volumes, helping investors to conduct market trend analysis, risk assessment, and anomaly detection.
• Weather forecastThe model can analyze meteorological data, such as temperature, humidity, and wind speed, to provide weather forecasts and disaster warnings. Through natural language interaction, users can obtain weather trend analysis and related suggestions.
• Industrial production optimizationThe ChatTS-14B can be used to monitor the operating status of production equipment, such as temperature, pressure, and vibration, to predict equipment failures in advance and optimize production processes. This helps improve production efficiency and reduce maintenance costs.
• HealthcareIn the medical field, ChatTS-14B can analyze patients' vital signs data (such as heart rate, blood pressure, and blood sugar) to assist doctors in monitoring and diagnosing their conditions. It can also help analyze time-series data such as electrocardiograms (ECG) to provide diagnostic suggestions.
• Intelligent Operations and Maintenance (AIOps)ChatTS-14B can analyze system monitoring metrics such as CPU utilization, memory utilization, and network latency to quickly pinpoint the cause of faults and provide diagnostic suggestions. Through natural language dialogue, operations and maintenance personnel can troubleshoot problems more efficiently.