A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights. (September 2016)

• Record Type: Journal Article
• Authors: Sharma, Ashish; Mehrotra, Raj; Li, Jingwan; Jha, Sanjeev
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 271
• Page End: 275
• Publication Date: 2016-09
• Subjects: Nonparametric prediction -- Partial Informational Correlation -- Partial Weight -- k-nearest neighbor (knn) -- Partial Mutual Information (PMI); Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.05.021
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Identification of system predictors forms the first step towards formulating a predictive model. Approaches for identifying such predictors are often limited by the need to assume a relationship between the predictor and response. To address this limitation, (Sharma and Mehrotra, 2014) presented a nonparametric predictive model using Partial Informational Correlation (PIC) and Partial Weights (PW). This study describes the open source Nonparametric Prediction (NPRED) R-package. NPRED identifies system predictors using the PIC logic, and predicts the response using a k-nearest-neighbor regression formulation based on a PW based weighted Euclidean distance. The capabilities of the package are demonstrated using synthetic examples and a real application of predicting seasonal rainfall in the Warragamba dam near Sydney, Australia. The results show clear improvements in predictability as compared to the use of linear predictive alternatives, as well as nonparametric alternatives that use an un-weighted Euclidean distance. Highlights: Open source R package NPRED for system identification and prediction. Estimate Partial Informational Correlation (PIC) and Partial Weight (PW). Improves predictability compared to existing alternatives.