TY - GEN
T1 - Multi-source transfer learning with ensemble for financial time series forecasting
AU - He, Qi Qiao
AU - Pang, Patrick Cheong Iao
AU - Si, Yain Whar
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - 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 openaccess large-scale datasets. However, in financial domain, the lengths of individual time series are relatively short and singlesource 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.
AB - 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 openaccess large-scale datasets. However, in financial domain, the lengths of individual time series are relatively short and singlesource 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.
KW - Artificial neural networks
KW - Financial time series forecasting
KW - Multi-source transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85114421082&partnerID=8YFLogxK
U2 - 10.1109/WIIAT50758.2020.00034
DO - 10.1109/WIIAT50758.2020.00034
M3 - Conference contribution
AN - SCOPUS:85114421082
T3 - Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
SP - 227
EP - 233
BT - Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
A2 - He, Jing
A2 - Purohit, Hemant
A2 - Huang, Guangyan
A2 - Gao, Xiaoying
A2 - Deng, Ke
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
Y2 - 14 December 2020 through 17 December 2020
ER -