[VLDB' 25] ChatTS-14B Model

[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 features native support for multi-variate time series data with any length and range of values. With ChatTS, you can easily understand and reason about both the shape features and value features in the time series. ChatTS can also be integrated into existing LLM pipelines for more time series-related applications, leveraging existing inference frameworks such as vLLMs.

Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data:

Link to the paper

Link to the Github repository

Usage

• This model is fine-tuned on the QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) model. For more usage details, please refer to the README.md in the ChatTS repository.
• An example usage of ChatTS (with HuggingFace):

from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
import torch
import numpy as np

# Load the model, tokenizer and processor
model = AutoModelForCausalLM.from_pretrained("./ckpt", trust_remote_code=True, device_map="auto", torch_dtype='float16')
tokenizer = AutoTokenizer.from_pretrained("./ckpt", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("./ckpt", trust_remote_code=True, tokenizer=tokenizer)
# Create time series and prompts
timeseries = np.sin(np.arange(256) / 10) * 5.0
timeseries[100:] -= 10.0
prompt = f"I have a time series length of 256: <ts><ts/>. Please analyze the local changes in this time series."
# Apply Chat Template
prompt = f"<|im_start|>system
You are a helpful assistant.<|im_end|><|im_start|>user
{prompt}<|im_end|><|im_start|>assistant
"
# Convert to tensor
inputs = processor(text=[prompt], timeseries=[timeseries], padding=True, return_tensors="pt")
# Model Generate
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0][len(inputs['input_ids'][0]):], skip_special_tokens=True))

Reference

• QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
• transformers (https://github.com/huggingface/transformers.git)
• ChatTS Paper

License

This model is licensed under the Apache License 2.0.

Cite

@article{xie2024chatts,
  title={ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning},
  author={Xie, Zhe and Li, Zeyan and He, Xiao and Xu, Longlong and Wen, Xidao and Zhang, Tieying and Chen, Jianjun and Shi, Rui and Pei, Dan},
  journal={arXiv preprint arXiv:2412.03104},
  year={2024}
}