Transfer Learning 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

27 Citations (Scopus)


Time-series are widely used for representing non-stationary data such as weather information, health related data, economic and stock market indexes. Many statistical methods and traditional machine learning techniques are commonly used for forecasting time series. With the development of deep learning in artificial intelligence, many researchers have adopted new models from artificial neural networks for forecasting time series. However, poor performance of applying deep learning models in short time series hinders the accuracy in time series forecasting. In this paper, we propose a novel approach to alleviate this problem based on transfer learning. Existing work on transfer learning uses extracted features from a source dataset for prediction task in a target dataset. In this paper, we propose a new training strategy for time-series transfer learning with two source datasets that outperform existing approaches. The effectiveness of our approach is evaluated on financial time series extracted from stock markets. Experiment results show that transfer learning based on 2 data sets is superior than other base-line methods.

Original languageEnglish
Title of host publicationPRICAI 2019
Subtitle of host publicationTrends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsAbhaya C. Nayak, Alok Sharma
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030299101
Publication statusPublished - 2019
Externally publishedYes
Event16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019 - Yanuka Island, Fiji
Duration: 26 Aug 201930 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11671 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019
CityYanuka Island


  • Artificial neural networks
  • Financial time series
  • Forecasting
  • Transfer learning


Dive into the research topics of 'Transfer Learning for Financial Time Series Forecasting'. Together they form a unique fingerprint.

Cite this