کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6962463 | 1452267 | 2016 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights
ترجمه فارسی عنوان
ابزار برنامه نویسی برای پیش بینی سیستم غیر پارامتر با استفاده از همبستگی اطلاعات جزئی و وزن های جزئی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 83, September 2016, Pages 271-275
Journal: Environmental Modelling & Software - Volume 83, September 2016, Pages 271-275
نویسندگان
Ashish Sharma, Raj Mehrotra, Jingwan Li, Sanjeev Jha,