Comparison between Time Series Analysis and Mode Decomposition on the Prediction of Bank of China Stock Price

Weiqian Zhao, Huiling Huang, Jiayi Shen, Yuchen Xing

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. Successful stock price forecasting can help investors get the maximum benefits. This paper proposes to add machine learning to the original traditional time series analysis method, then improve the accuracy of Bank of China stock price prediction by using neural network and modal decomposition method. Back Propagation (BP) neural network acts on time series method will be shown at first. Then, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition (CEEMDAN) algorithms will act with BP neural network respectively. After analyzing and contrasting, the final proposed model will be obtained in the summary at the end of the paper, which can predict stock price of Bank of China more accurately.

Original languageEnglish
Title of host publicationICIBE 2021 - 2021 7th International Conference on Industrial and Business Engineering
PublisherAssociation for Computing Machinery
Pages168-173
Number of pages6
ISBN (Electronic)9781450390644
DOIs
Publication statusPublished - 27 Sept 2021
Externally publishedYes
Event7th International Conference on Industrial and Business Engineering, ICIBE 2021 - Virtual, Online, China
Duration: 27 Sept 202129 Sept 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Industrial and Business Engineering, ICIBE 2021
Country/TerritoryChina
CityVirtual, Online
Period27/09/2129/09/21

Keywords

  • Complete Ensemble Empirical Mode Decomposition
  • Empirical Mode Decomposition
  • Ensemble Empirical Mode Decomposition
  • Mode Decomposition
  • Neural Network
  • Stock Price Prediction
  • Time Series Analysist

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