A Review of Data-Trait-Driven Univariate Predictive Modeling: Canonical Frameworks and Future Directions

Prediction is the foundation of decision-making and planning, including 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 models 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 predictive 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 knowledge-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.