TY - GEN
T1 - Comparison between Time Series Analysis and Mode Decomposition on the Prediction of Bank of China Stock Price
AU - Zhao, Weiqian
AU - Huang, Huiling
AU - Shen, Jiayi
AU - Xing, Yuchen
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/9/27
Y1 - 2021/9/27
N2 - 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.
AB - 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.
KW - Complete Ensemble Empirical Mode Decomposition
KW - Empirical Mode Decomposition
KW - Ensemble Empirical Mode Decomposition
KW - Mode Decomposition
KW - Neural Network
KW - Stock Price Prediction
KW - Time Series Analysist
UR - http://www.scopus.com/inward/record.url?scp=85123048158&partnerID=8YFLogxK
U2 - 10.1145/3494583.3494644
DO - 10.1145/3494583.3494644
M3 - Conference Proceeding
AN - SCOPUS:85123048158
T3 - ACM International Conference Proceeding Series
SP - 168
EP - 173
BT - ICIBE 2021 - 2021 7th International Conference on Industrial and Business Engineering
PB - Association for Computing Machinery
T2 - 7th International Conference on Industrial and Business Engineering, ICIBE 2021
Y2 - 27 September 2021 through 29 September 2021
ER -