Financial Time Series Forecasting with the Deep Learning Ensemble Model

Kaijian He, Qian Yang, Lei Ji, Jingcheng Pan and Yingchao Zou ()
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Kaijian He: College of Tourism, Hunan Normal University, Changsha 410081, China
Qian Yang: College of Tourism, Hunan Normal University, Changsha 410081, China
Lei Ji: Shanghai Kaiyu Information Technology Co., Ltd., Shanghai 202179, China
Jingcheng Pan: School of Business, Hunan University of Science and Technology, Xiangtan 411201, China
Yingchao Zou: College of Tourism, Hunan Normal University, Changsha 410081, China

Mathematics, 2023, vol. 11, issue 4, 1-15

Abstract: With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operation and management. In this paper, we propose a new financial time series forecasting model based on the deep learning ensemble model. The model is constructed by taking advantage of a convolutional neural network (CNN), long short-term memory (LSTM) network, and the autoregressive moving average (ARMA) model. The CNN-LSTM model is introduced to model the spatiotemporal data feature, while the ARMA model is used to model the autocorrelation data feature. These models are combined in the ensemble framework to model the mixture of linear and nonlinear data features in the financial time series. The empirical results using financial time series data show that the proposed deep learning ensemble-based financial time series forecasting model achieved superior performance in terms of forecasting accuracy and robustness compared with the benchmark individual models.

Keywords: financial time series; convolutional neural network; long short-term memory; ensemble forecasting model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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