Financial time series forecasting model based on CEEMDAN and LSTM
By Jianmei Cao, Zhi Li, Jian Li. Physica A: Statistical Mechanics and its Applications, 2019
643 citations recorded by the source corpus.
Abstract In order to improve the accuracy of the stock market prices forecasting, two hybrid forecasting models are proposed in this paper which combine the two kinds of empirical mode decomposition (EMD) with the long short-term memory (LSTM). The financial time series is a kind of non-linear and non-stationary random signal, which can be decomposed into several intrinsic mode functions of different time scales by the original EMD and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). To ensure the effect of historical data onto the prediction result, the LSTM prediction models are established for all each characteristic series from EMD and CEEMDAN deposition. The final prediction results are obtained by reconstructing each prediction series. The forecasting performance of the proposed models is verified by linear regression analysis of the major global stock market indices. Compared with single LSTM model, support vector machine (SVM), multi-layer perceptron (MLP) and other hybrid models, the experimental results show that the proposed models display a better performance in one-step-ahead forecasting of financial time series.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/4c7005ca9650ef42d734be495fa39851dde9e827
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In Computer Vision and Pattern Recognition
In Electrical and Electronic Engineering
In Management Science and Operations Research
Citations in the corpus
- Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…Chung & Shin (2018); Gao & Chai (2018); Hossain et al. (2018); Lei (2018); Wu & Gao (2018); Tsang et al. (2018); Yan & Ouyang (2018); de A. Arajo et al. (2019); Cao et al. (2019); Chen et al. (2019); Jin et al. (2019); Karathanasopoulos & Osman (2019); Kim & Kim (2019); Kumar et al. (2019);…”