Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends

Authors:
Harya Widiputra;Russel Pears;Nikola Kasabov
Affiliations:
The Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;The Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;The Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

Citing 6
Cited 1

Induction: Processes of Inference, Learning, and Discovery

Induction: Processes of Inference, Learning, and Discovery
Approximation by fully complex multilayer perceptrons

Neural Computation
Multivariate Time Series Prediction via Temporal Classification

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach

Pattern Recognition Letters
Hierarchical Clustering of Time-Series Data Streams

IEEE Transactions on Knowledge and Data Engineering
A Novel Evolving Clustering Algorithm with Polynomial Regression for Chaotic Time-Series Prediction

ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II

Evolving spiking neural networks and neurogenetic systems for spatio- and spectro-temporal data modelling and pattern recognition

WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Time-series prediction has been very well researched by both the Statistical and Data Mining communities. However the multiple time-series problem of predicting simultaneous movement of a collection of time sensitive variables which are related to each other has received much less attention. Strong relationships between variables suggests that trajectories of given variables that are involved in the relationships can be improved by including the nature and strength of these relationships into a prediction model. The key challenge is to capture the dynamics of the relationships to reflect changes that take place continuously over time. In this research we propose a novel algorithm for extracting profiles of relationships through an evolving clustering method. We use a form of non-parametric regression analysis to generate predictions based on the profiles extracted and historical information from the past. Experimental results on a real-world climatic data reveal that the proposed algorithm outperforms well established methods of time-series prediction.