Multilevel Information Granule Construction Model Based on Large Data Environment and Its Application in Time Series

Considering the uneven distribution of time series data in time windows, this paper designs an information granulation method based on the idea of “multigranularity,” and uses multilayer information granularity to construct the prediction model of time series. Firstly, a multigranularity time series model is established by applying binary relation on the time axis of time series data, and an evaluation effect function of time series data mining is introduced for the purpose of forecasting tasks. Based on this function, the optimal time granularity of prediction is found. In view of the uncertainty of time series, a multidimensional matrix model of attributes of sequence data is constructed, and the clustering category information of each attribute is obtained by clustering operation on the matrix model. Finally, the clustered data are classified by machine learning method to obtain classification knowledge. Prediction is achieved by classifying knowledge. Finally, the ICU data were used to predict the life and death of ICU patients to test the predictive effect of granular computing on time series data. The experimental results show the following: (1) the proposed method is superior to the traditional time series analysis and modeling methods. (2) When EM clustering algorithm, optimal time granularity and ID3 decision tree algorithm are used, the prediction rate of ICU mortality can reach 92.13% by using ICU time series data.