Transfer Learning in Financial Time Series with Gramian Angular Field (Student Abstract)

Hou-Wan Long, On-In Ho, Qi-Qiao He, Yain-Whar Si

AAAI 2025 pp. 29418-29420

doi:10.1609/AAAI.V39I28.35272 /aaai/2025/long2025aaai-transfer/

Abstract

Transfer learning enhances model performance in financial time series by leveraging data from related domains. The selection of appropriate source domains is crucial to avoid negative transfer. We propose using Gramian Angular Field (GAF) transformations to improve time series similarity functions for better domain alignment. Extensive experiments with DNN and LSTM models show that GAF-based similarity functions, specifically Coral (GAF) for DNN and CMD (GAF) for LSTM, significantly reduce prediction errors, demonstrating their effectiveness in complex financial environments.

AAAI Semantic Scholar

Cite

Text

Long et al. "Transfer Learning in Financial Time Series with Gramian Angular Field (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35272

Markdown

[Long et al. "Transfer Learning in Financial Time Series with Gramian Angular Field (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/long2025aaai-transfer/) doi:10.1609/AAAI.V39I28.35272

BibTeX

@inproceedings{long2025aaai-transfer,
  title     = {{Transfer Learning in Financial Time Series with Gramian Angular Field (Student Abstract)}},
  author    = {Long, Hou-Wan and Ho, On-In and He, Qi-Qiao and Si, Yain-Whar},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29418-29420},
  doi       = {10.1609/AAAI.V39I28.35272},
  url       = {https://mlanthology.org/aaai/2025/long2025aaai-transfer/}
}