Article: Nearest neighbour approach with non-parametric regression analysis for multiple time-series modelling and predictions Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2015 Vol.10 No.3 pp.253 - 279 Abstract: Time-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. Inderscience Publishers - linking academia, business and industry through research

Title: Nearest neighbour approach with non-parametric regression analysis for multiple time-series modelling and predictions

Authors: Harya Widiputra

Addresses: Faculty of Information Technology, Perbanas Institute, Indonesia

Abstract: Time-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.

Keywords: multiple time-series prediction; non-parametric regression analysis; nearest neighbour; modelling; kernel regression.

DOI: 10.1504/IJBIDM.2015.071326

International Journal of Business Intelligence and Data Mining, 2015 Vol.10 No.3, pp.253 - 279

Received: 19 Feb 2015
Accepted: 15 Mar 2015
Published online: 20 Aug 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Nearest neighbour approach with non-parametric regression analysis for multiple time-series modelling and predictions

Keep up-to-date