Paper Info
Title
Nearest neighbour approach with non-parametric regression analysis for multiple time-series modelling and predictions
Abstract
AbstractTime-series prediction is an intensively researched area, yet most studies in this field have focused on predicting movements of a single series only, whilst prediction of multiple time-series based on patterns of interaction between multiple time-series has received very little attention. On the other hand, findings in various studies show that given a multiple time-series data there exist patterns of relationship between the observed variables, and being able to model them would lead to the possibility of building a more accurate model to predict their future values. Nevertheless, as real-world systems change dynamically over time, having a single model to explain simultaneous movement of multiple time-series will not be sufficient. To address this problem, the paper presents an algorithm that is capable of building a new decision model on-the-fly based on the state of relationships, between observed variables at a particular time-point. The proposed algorithm utilises non-parametric regression analysis to extract profiles of relationship between observed variables and then employs the nearest neighbour approach to find appropriate conditions from the past. Experimental results on a real-world dataset suggest that the implementation of kernel regression merged with nearest neighbour approach shows that it outperforms established methods such as multiple linear regression and multi-layer perceptron.
Year
DOI
Venue
2015
10.1504/IJBIDM.2015.071326
Periodicals
Field
DocType
Volume
Data mining,Regression diagnostic,Regression analysis,Computer science,Nonparametric regression,Proper linear model,Artificial intelligence,Decision model,Perceptron,Kernel regression,Machine learning,Linear regression
Journal
10
Issue
ISSN
Citations 
3
1743-8195
0
PageRank 
References 
Authors
0.34
18
1
Name
Order
Citations
PageRank
Harya Widiputra1324.12