Prediction is the foundation of decision-making and planning,includ-ing univariate and multivariate predictive modeling.Univariate predictive modeling,which only utilizes the historical values of time series,has been widely applied in fields such as agriculture,energy,environment,and finance.Data-trait-driven mod-els are based on the traits of the data itself to select models and predict future trends.This article focuses on the research paradigm of data-trait-driven predic-tive modeling.Through literature review and summary,seven typical frameworks are proposed,including expert knowledge-based,data trait-driven,expert knowledge-driven decomposition-ensemble,expert knowledge-driven decomposition-clustering-reconstruction-ensemble,data-knowledge hybrid-driven decomposition-ensemble,data-knowledge hybrid-driven decomposition-clustering-reconstruction-ensemble,and know-ledge-data hybrid-driven decomposition-ensemble.Then,the methods of data trait classification and identification,decomposition-ensemble,clustering-reconstruction,and prediction methods are reviewed.Finally,future research directions and typical scientific problems are discussed,including the identification and verification of mixed data traits,intelligent predictive modeling,clustering-reconstruction new methods,prediction-ensemble new methods,and large-scale models for time series data,aiming to provide reference for the research of data-trait-driven univariate prediction theory and methods.
万方数据
维普
