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A Combined Model for Time Series Prediction in Financial Markets

  • Hongbo Sun
  • Chenkai GuoEmail author
  • Jing Xu
  • Jingwen Zhu
  • Chao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)

Abstract

Time series prediction is not easy to achieve high accuracy. non-linear and unstable characteristics make the time series prediction difficult. The variety of dataset make the prediction result debatable. In order to solve this problem, in this paper we propose a deep learning prediction method based on decomposition, reconstruction and combination, which combines ways of communication field. The model is decomposed by Empirical Mode Decomposition, Principal Component Analysis and Long Short-Term Memory networks (EPL below). And also, the proposed interval EPL (IEPL below) improve and consummate the EPL model. The EPL and IEPL experiment results will bring average 5% higher accuracy than that of existing research.

Keywords

Time series prediction Deep learning EPL IEPL 

Notes

Acknowledgement

This research work here is supported by the Science and Technology Planning Project of Tianjin (Grant No. 17JCZDJC30700 and 17YFZCGX00610).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer and Control of EngineeringNankai UniversityTianjinChina

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