A deep learning framework for financial time series using stacked   autoencoders and long-short term memory

doi:10.1371/journal.pone.0180944

CORC > 北京大学 > 地球与空间科学学院

	A deep learning framework for financial time series using stacked autoencoders and long-short term memory
	Bao, Wei ; Yue, Jun ; Rao, Yulei
刊名	PLOS ONE
	2017
关键词	ARTIFICIAL NEURAL-NETWORKS STOCK-MARKET COMPONENT ANALYSIS CLASSIFICATION MODEL INDEX REPRESENTATION RECOGNITION PREDICTION REGRESSION
DOI	10.1371/journal.pone.0180944
英文摘要	The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.; National Natural Science Foundation of China [71372063, 71673306]; SCI(E); SSCI; ARTICLE; 7; 12
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/469075]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Bao, Wei,Yue, Jun,Rao, Yulei. A deep learning framework for financial time series using stacked autoencoders and long-short term memory[J]. PLOS ONE,2017.
APA	Bao, Wei,Yue, Jun,&Rao, Yulei.(2017).A deep learning framework for financial time series using stacked autoencoders and long-short term memory.PLOS ONE.
MLA	Bao, Wei,et al."A deep learning framework for financial time series using stacked autoencoders and long-short term memory".PLOS ONE (2017).