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Auto Encoder Based = Abnormal Fluctuations Monitoring Method

A variety of factors affect the changes of stock prices, and the data sets have large time scales and are closely correlated. Accurate anomaly detection in the stock market can help investors evaluate and detect stock prices in different areas of various problems. In order to help stockholders to be more strategic and productive in stock capital, we are prose an algorithm that is both concise and effective. However, we found that a group of people make profits by manipulating the market to influence the stock price. This is just one of many factors that affect stock prices. However, based on previous studies, we find that many anomaly analyses are based on some existing dead rules, making it difficult to improve the accuracy of the process. Moreover, in the stock market, the outlier detection model is not suitable for dynamic high-latitude target detection, we must consider various factors of node variables. Therefore, in this paper, we firstly obtained high-frequency trading data and trained a model. The comparison between the algorithm we proposed, and the other three baselines’ experimental results show that our proposed algorithm is more accurate than others, reducing the discriminant value of sequence fluctuation from 15% to 3%, and the screening result is closest to NYSE Rule 48, which has higher explanatory power and is more accurate. (Abstract)