Research on trend prediction of component stock in fuzzy time series based on deep forest

Abstract

With the continuous development of machine learning and the increasing complexity of financial data analysis, it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry. To improve the effectiveness of stock trend prediction and solve the problems in time series data processing, this paper combines the fuzzy affiliation function with stock-related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index. Meanwhile, in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge, this paper combines the deep forest model to train the stock data separately. The experimental results show that (1) the accuracy of the extreme random forest and the accuracy of the multi-grain cascade forest are both higher than that of the gated recurrent unit (GRU) model when the un-fuzzy index-adjusted dataset is used as features for input, (2) the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index-adjusted dataset as features for input, (3) the accuracy of the fuzzy index-adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un-fuzzy index-adjusted dataset as features for inputting the extreme random forest and (4) the average accuracy of the fuzzy index-adjusted dataset as features for inputting multi-grain cascade forest increased by 5.67%.