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

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ID: 42554
2017
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Abstract
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.
Reference Key
bao2017aplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bao, Wei;Yue, Jun;Rao, Yulei;
Journal PloS one
Year 2017
DOI
10.1371/journal.pone.0180944
URL
Keywords Keywords not found

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