Abstract
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.
Highlights
Forecasting is an important means of reducing risk and increase revenue in financial sector
Stock price prediction models can be divided into two categories: statistical model and artificial intelligence model
Given the chaotic state of stock market values, Song and Chissom propose the fuzzy time series (FTS) forecasting model [19,20,21] based on the fuzzy set theory for the first time.other scholars try to combine neural network and fuzzy time series, such as Aladag et al [22]use BPNN to determine fuzzy relations in their fuzzy time series method
Summary
Forecasting is an important means of reducing risk and increase revenue in financial sector. A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and BP Neural Network. Given the chaotic state of stock market values, Song and Chissom propose the fuzzy time series (FTS) forecasting model [19,20,21] based on the fuzzy set theory for the first time.other scholars try to combine neural network and fuzzy time series, such as Aladag et al [22]use BPNN to determine fuzzy relations in their fuzzy time series method. We propose a hybrid forecasting method called High-orderfuzzy-fluctuation-Trends-based Back Propagation(HTBP)neural network modal In such a model, the original data are first decomposed into multiple layers by the High-Order-FuzzyFluctuation series. Forecast test time series For each data in the test time series, its future number can be forecasted according to Eq (7), based on the result of the output of the BP Neural NetworkMachine Learning, its n-order fuzzy-fluctuation trends.
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