Chattime 1 7B Base

🚀 ChatTime: A Multimodal Time Series Foundation Model

ChatTime is a unified framework for time series and text processing. It models time series as a foreign language, offering zero - shot forecasting and supporting bimodal input/output for time series and text. The model's performance is verified through experiments, and four multimodal datasets are created.

✨ Features

• Innovative Modeling: Models time series as a foreign language.
• Multimodal Support: Supports bimodal input/output for time series and text.
• Zero - Shot Forecasting: Provides zero - shot forecasting capability.
• Proven Performance: Demonstrates superior performance through experiments.

🚀 Quick Start

For details on ChatTime models, training data and procedures, and experimental results, please refer to the arXiv.

As shown in Figure 1(b), during the continuous pre - training stage, we pre - train [LLaMA - 2 - 7B - Base](https://huggingface.co/meta - llama/Llama - 2 - 7b - hf) on [ChengsenWang/ChatTime - 1 - Pretrain - 1M](https://huggingface.co/datasets/ChengsenWang/ChatTime - 1 - Pretrain - 1M), resulting in [ChengsenWang/ChatTime - 1 - 7B - Base](https://huggingface.co/ChengsenWang/ChatTime - 1 - 7B - Base).

💻 Usage Examples

Basic Usage

We present three minimal examples showing how to perform the multimodal time series analysis using the ChatTime model. The detailed code is available in the Github.

Zero - Shot Time Series Forecasting

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from model.model import ChatTime

dataset = "Traffic"
hist_len = 120
pred_len = 24
model_path = "ChengsenWang/ChatTime-1-7B-Chat"

df = pd.read_csv(f"./dataset/{dataset}.csv")
hist_data = np.array(df["Hist"].apply(eval).values.tolist())[:, -hist_len:][0]
pred_data = np.array(df["Pred"].apply(eval).values.tolist())[:, :pred_len][0]

model = ChatTime(hist_len=hist_len, pred_len=pred_len, model_path=model_path)

out = model.predict(hist_data)

hist_x = np.linspace(0, hist_len-1, hist_len)
pred_x = np.linspace(hist_len, hist_len+pred_len-1, pred_len)

plt.figure(figsize=(8, 2), dpi=500)
plt.plot(hist_x, hist_data, color='#000000')
plt.plot(pred_x, pred_data, color='#000000', label='true')
plt.plot(pred_x, out, color='#FF7F0E', label='pred')
plt.axvline(hist_len, color='red')
plt.legend(loc="upper left")
plt.show()

Context - Guided Time Series Forecasting

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from model.model import ChatTime

dataset = "PTF"
hist_len = 120
pred_len = 24
model_path = "ChengsenWang/ChatTime-1-7B-Chat"

df = pd.read_csv(f"./dataset/{dataset}.csv")
hist_data = np.array(df["Hist"].apply(eval).values.tolist())[:, -hist_len:][0]
pred_data = np.array(df["Pred"].apply(eval).values.tolist())[:, :pred_len][0]
context = df["Text"].values[0]

model = ChatTime(hist_len=hist_len, pred_len=pred_len, model_path=model_path)

out_text = model.predict(hist_data, context)
out = model.predict(hist_data)

hist_x = np.linspace(0, hist_len-1, hist_len)
pred_x = np.linspace(hist_len, hist_len+pred_len-1, pred_len)

plt.figure(figsize=(8, 2), dpi=500)
plt.plot(hist_x, hist_data, color='#000000')
plt.plot(pred_x, pred_data, color='#000000', label='true')
plt.plot(pred_x, out_text, color='#FF7F0E', label='pred_text')
plt.plot(pred_x, out, color='#1F77B4', label='pred')
plt.axvline(hist_len, color='red')
plt.legend(loc="upper left")
plt.show()

Time Series Question Answering

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from model.model import ChatTime

dataset = "TSQA"
model_path = "ChengsenWang/ChatTime-1-7B-Chat"

df = pd.read_csv(f"./dataset/{dataset}.csv")
series = np.array(df["Series"].apply(eval).values.tolist())[0]
question = df["Question"].values[0]
answer = df["Answer"].values[0]

model = ChatTime(model_path=model_path)

out = model.analyze(question, series)

plt.figure(figsize=(8, 2), dpi=500)
plt.plot(series, color='#000000')
plt.show()

print(question)
print(f"\n{out} / {answer}\n")

📝 Citation

If you find this repo or our work useful for your research, please consider citing the paper:

@inproceedings{
  author    = {Chengsen Wang and Qi Qi and Jingyu Wang and Haifeng Sun and Zirui Zhuang and Jinming Wu and Lei Zhang and Jianxin Liao},
  title     = {ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
}

📪 Contact

If you have any question, please contact cswang@bupt.edu.cn.