DeepLight: Reconstructing High-Resolution Observations of Nighttime Light with Multi-Modal Remote Sensing Data
Abstract
Forecasting stock prices is complex and challenging. Uncovering correlations among stocks has proven to enhance stock price forecasting. However, existing correlation discovery methods, such as concept-based methods, are slow, inaccurate, and limited by their reliance on predefined concepts and manual analysis. In this paper, we propose COGRASP, a novel approach for stock price forecasting that constructs stock co-occurrence graphs automatically by analyzing rapidly updated sources such as reports, newspapers, and social media. Besides, we aggregate forecasts across multiple timescales (i.e., long-, medium-, and short-term) to capture multi-timescale trends fluctuations, thereby enhancing price forecasting accuracy. In experiments with real-world open-source stock market data, COGRASP outperforms state-of-the-art methods.
Cite
Text
Zhang et al. "DeepLight: Reconstructing High-Resolution Observations of Nighttime Light with Multi-Modal Remote Sensing Data." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/837Markdown
[Zhang et al. "DeepLight: Reconstructing High-Resolution Observations of Nighttime Light with Multi-Modal Remote Sensing Data." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/zhang2024ijcai-deeplight/) doi:10.24963/ijcai.2024/837BibTeX
@inproceedings{zhang2024ijcai-deeplight,
title = {{DeepLight: Reconstructing High-Resolution Observations of Nighttime Light with Multi-Modal Remote Sensing Data}},
author = {Zhang, Lixian and Dong, Runmin and Yuan, Shuai and Zhang, Jinxiao and Chen, Mengxuan and Zheng, Juepeng and Fu, Haohuan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {7563-7571},
doi = {10.24963/ijcai.2024/837},
url = {https://mlanthology.org/ijcai/2024/zhang2024ijcai-deeplight/}
}