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Financial Time Series Forecasting based on Novel CEEMDAN-LSTM-BN Network

Author(s)

Shunwei Dou

Affiliation(s)

Xi’an Jiaotong-liverpool University, Suzhou, China

Abstract

Long-short term memory(LSTM) is a state-of-art and widely used model to forecast financial time series. However, primitive LSTM networks do not perform well due to over-fitting problems of the deep learning model and non-linear and non-stationary characteristics of financial time series data. Thus, this paper proposed a novel hybrid network based on LSTM to solve the two problems. To avoid over-fitting, the modified LSTM-BN network consists of two LSTM layers, two Batch Normalization(BN) layers following each LSTM layer and a dropout layer. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is an outstanding data frequency decomposition technique which can decompose original time series into several intrinsic mode functions and a residue. Each of the intrinsic mode function and the residue would be processed by LSTM-BN and the final prediction results are obtained by reconstructing each predictive series. The advantages of the proposed networks are verified by comparing to primitive LSTM, other hybrid models and some famous machine learning model such as Support Vector Machine(SVM), Autoregressive(AR) and Random Forest. Moreover, the robustness of the networks are assessed by numerical experiments on different stock indices datasets including “Standard’s & Poor’s 500 Index”, “Nikkei 225”, “Heng Seng Index” and “Deutscher Aktienindex Index”.

Keywords

LSTM Networks; SVM; CEEMDAN

References

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