Multi-source transfer learning with ensemble for financial time series forecasting

Qi Qiao He, Patrick Cheong Iao Pang, Yain Whar Si

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
EditorsJing He, Hemant Purohit, Guangyan Huang, Xiaoying Gao, Ke Deng
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages227-233
Number of pages7
ISBN (Electronic)9781665419246
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes
Event2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 - Virtual, Online
Duration: 14 Dec 202017 Dec 2020

Publication series

NameProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020

Conference

Conference2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
CityVirtual, Online
Period14/12/2017/12/20

Keywords

  • Artificial neural networks
  • Financial time series forecasting
  • Multi-source transfer learning

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