Modelling and learning cause-effect — application in frost forecast

Abstract

Abstract With the recent achievements in real world applications, being able to learn cause-effect has been expected as a new aim of artificial intelligence (AI) and machine learning (ML). As a preliminary attempt, causal modelling has been proposed for capturing relation between past observation of environment factors and future event of frost. This article continues explore methods of modelling and learning cause-effect relation in frost forecast. It first argues that the relation between environment factors and frost event is of cause-effect more than correlation, then discusses the involvement of time in modelling such cause-effect. Methods of modelling are discussed with their assumptions and rational behind. Performance comparison is provided.