Time Series Data Reconstruction Method Based on Probability Statistics and Machine Learning

Chen, Haiying; Liu, Yinghua

In order to improve the reconstruction ability of time series data under probability statistical model, a time series data reconstruction method based on machine learning is proposed. The time series data distribution structure model under probability statistical model is constructed. The spatial multi-sensor information sampling method is used to sample the time series data information flow under the probability statistical model, and the phase space reconstruction method is combined to reconstruct the time series data information structure under the probability statistical model. The probability statistical model is established to decompose the time series data, and the distributed grid computing method is used to extract the big data association features of the time series data under the probability statistical model. Combined with the adaptive weight learning method, the optimal control of the scheduling is carried out. The big data cross-domain scheduling of the time series data under the probabilistic statistical model is realized under the support vector machine learning mode. The simulation results show that the method has good adaptability to time series data cross-domain scheduling under the probability and statistics model, and the load balance of data output is strong.