Abstract: Derivative industries in the field of science and technology have accumulated a large amount of high-dimensional time series data due to the general existence of strong time constraints.Severe data pressure makes traditional data modeling and prediction methods limited by data scale and attribute dimensions.Services supporting high-quality put forward higher requirements for big data intelligent prediction technology.How to improve the prediction performance at the data level is a main problem that needs to be solved urgently at this stage.Combined with the above problems,a feature re-abstraction(FRA) algorithm for multivariate time series data is proposed.First,the RobustSTL decomposition algorithm is used to extract trend and seasonality features(TSFs),realize the second-order abstraction of features of multivariate data,and replace the traditional extraction strategy of “labels are features” with “abstract is features”.Then,the correlation strength between the TSFs captured by the re-abstract technology and the target parameters is evaluated by the calculation result of the Pearson correlation coefficient,which confirms the data value of the TSF.On the basis of FRA algorithm,combined with deep learning model,a data-driven multivariate time series prediction algorithm is constructed,and the effectiveness of FRA algorithm is verified by the prediction effect.Experimental results show that the introduction of TSFs as the training vector of the data-driven model can maintain the characteristics of data dimensionality reduction,noise reduction and strong correlation,so as to avoid model overfitting and alleviate model underfitting,and improve the accuracy and robustness of time series prediction algorithms.

Key words: Multivariate time series data, Multivariate time series forecasting algorithms, Feature re-abstraction(FRA), Trend and seasonality feature(TSF), Correlation assessment