Financial Sequence Prediction Based on Swarm Intelligence Algorithms of Internet of Things

Abstract

In order to accurately predict the financial time series and stock price fluctuation, in this study, metadata was used to ensure the objectivity of data for most of the swarm intelligence algorithms of the Internet of Things. The results show that the combination of theoretical methods and financial time series forecasting model can significantly improve the forecasting effect. At the same time, the proposed fuzzy theory also gives a totally different perspective to the modeling of financial time series which has stochastic and uncertain characteristics. Therefore, swarm intelligence algorithm and fuzzy theory are effectively combined, and their combination is very useful for the improvement of two financial fuzzy time series models. Because of their combination, the problem of stock price forecasting with different orders and different factors is well solved. Besides, it also provides two new solutions for such problems respectively as well as additional model choices for investors to avoid risks and improve returns.