arXiv subjects

Qi-Qiao He

Publications and source records attributed to Qi-Qiao He.

2 recordsLinked to original sources

arXiv2025

Transfer Learning in Financial Time Series with Gramian Angular Field

In financial analysis, time series modeling is often hampered by data scarcity, limiting neural network models' ability to generalize. Transfer learning mitigates this by leveraging data from similar domains, but selecting appropriate source domains is crucial to avoid negative transfer. This study enhances source domain selection in transfer learning by introducing Gramian Angular Field (GAF) transformations to improve time series similarity functions. We evaluate a comprehensive range of baseline similarity functions, including both basic and state-of-the-art (SOTA) functions, and perform extensive experiments with Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) networks. The results demonstrate that GAF-based similarity functions significantly reduce prediction errors. Notably, Coral (GAF) for DNN and CMD (GAF) for LSTM consistently deliver superior performance, highlighting their effectiveness in complex financial environments.

cs.CE↗

arXiv2021

Multi-source Transfer Learning with Ensemble for Financial Time Series Forecasting

Although transfer learning is proven to be effective in computer vision and natural language processing applications, it is rarely investigated in forecasting financial time series. Majority of existing works on transfer learning are based on single-source transfer learning due to the availability of open-access large-scale datasets. However, in financial domain, the lengths of individual time series are relatively short and single-source transfer learning models are less effective. Therefore, in this paper, we investigate multi-source deep transfer learning for financial time series. We propose two multi-source transfer learning methods namely Weighted Average Ensemble for Transfer Learning (WAETL) and Tree-structured Parzen Estimator Ensemble Selection (TPEES). The effectiveness of our approach is evaluated on financial time series extracted from stock markets. Experiment results reveal that TPEES outperforms other baseline methods on majority of multi-source transfer tasks.

cs.LG↗