A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

Autor: Hongjun Guan, Zongli Dai, Aiwu Zhao, Jie He
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: PLoS ONE, Vol 13, Iss 2, p e0192366 (2018)
Druh dokumentu: article
ISSN: 1932-6203
09313192
DOI: 10.1371/journal.pone.0192366
Popis: In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.
Databáze: Directory of Open Access Journals
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