Transfer Learning for Financial Time Series Forecasting

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

研究成果: Conference contribution同行評審

25 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題PRICAI 2019
主出版物子標題Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings
編輯Abhaya C. Nayak, Alok Sharma
發行者Springer Verlag
頁面24-36
頁數13
ISBN(列印)9783030299101
DOIs
出版狀態Published - 2019
對外發佈
事件16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019 - Yanuka Island, Fiji
持續時間: 26 8月 201930 8月 2019

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11671 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019
國家/地區Fiji
城市Yanuka Island
期間26/08/1930/08/19

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