k-Nearest-Neighbor by Differential Evolution for Time Series Forecasting

被引：0

作者：

De La Vega, Erick ^{[1
]}

Flores, Juan J. ^{[1
]}

Graff, Mario ^{[1
]}

机构：

[1] Univ Michoacana, Fac Ingn Elect, Div Estudios Posgrad, Morelia 58000, Michoacan, Mexico

来源：

NATURE-INSPIRED COMPUTATION AND MACHINE LEARNING, PT II | 2014年 / 8857卷

关键词：

Time Series Forecasting; Prediction; k-Nearest-Neighbor; Differential Evolution;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A framework for time series forecasting that integrates k-Nearest-Neighbors (kNN) and Differential Evolution (DE) is proposed. The methodology called NNDEF (Nearest Neighbor - Differential Evolution Forecasting) is based on knowledge shared from nearest neighbors with previous similar behaviour, which are then taken into account to forecast. NNDEF relies on the assumption that observations in the past similar to the present ones are also likely to have similar outcomes. The main advantages of NNDEF are the ability to predict complex nonlinear behavior and handling large amounts of data. Experiments have shown that DE can optimize the parameters of kNN and improve the accuracy of the predictions.

引用

页码：50 / 60

页数：11