A financial time series data mining method with different time granularity based on trend Division

Abstract

Stock research is an important field of Finance and time series research. Stock data research is also a typical financial time series problem. In the research of financial time series, there are many methods, such as model building, data mining, heuristic algorithm, machine learning, deep learning, and so on. VAR, ARIMA and other methods are widely used in practice. ARIMA and its combination methods have good processing effect on small data sets, but there are over fitting problems, which are difficult to process large data sets and data with different time granularity. At present, this paper takes the decision table transformation method of financial time series data as the research object, and puts forward the trend division method of financial time series based on different time granularity through the trend division of financial time series. On this basis, it puts forward the trend extreme point extraction method, and constructs the stock time series decision table according to the extreme point information and combined with the stock technical indicators, The decision table is verified by support vector machine based on the decision table. The research shows that the trend division method under different time granularity can transform the extreme point information into a decision table, which will not produce over fitting problem in practical application. It is an effective time series processing method, and provides a new research method for the future time series research with different granularity.