Modeling and extrapolation of high-dimensional data

Dariusz J Jakobczak

Modeling and extrapolation of high-dimensional data

EuroSciCon Conference & Expo on Robotics, Automation & Data Analytics
April 08-09, 2019 | Paris, France

Dariusz J Jakobczak

Koszalin University of Technology, Poland

Posters & Accepted Abstracts: Am J Compt Sci Inform Technol

Abstract

Economists, scientists, decision makers, academicians, researchers, advanced-level students, technology developers, and government officials will find this text useful in furthering their research exposure to pertinent topics in computer science, numerical analysis or operations research and assisting in furthering their own research efforts in these fields. Proposed method, called probabilistic features combination (PFC), is the method of 2D curve interpolation and extrapolation using the set of key points (knots or nodes). Nodes can be treated as characteristic points of data for modeling and analyzing. The model of data can be built by choice of probability distribution function and nodes combination. PFC modeling via nodes combination and parameter γ as probability distribution function enables value anticipation in risk analysis and decision making. Two-dimensional curve is extrapolated and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.