A Novel Evolving Clustering Algorithm with Polynomial Regression for Chaotic Time-Series Prediction

Authors:
Harya Widiputra;Henry Kho; Lukas;Russel Pears;Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;Faculty of Information and Technology, Swiss German University, BSD City, Indonesia;Cognitive Engineering Research Group (CERG), Universitas Katolik Atma Jaya, Indonesia;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

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Abstract

Time-series prediction has been a very well researched topic in recent studies. Some popular approaches to this problem are the traditional statistical methods e.g. multiple linear regression and moving average, and neural network with the Multi Layer Perceptron which has shown its supremacy in time-series prediction. In this study, we used a different approach based on evolving clustering algorithm with polynomial regressions to find repeating local patterns in a time-series data. To illustrate chaotic time-series data we have taken into account the use of stock price data from Indonesian stock exchange market and currency exchange rate data. In addition, we have also conducted a benchmark test using the Mackey Glass data set. Results showed that the algorithm offers a considerably high accuracy in time-series prediction and could also reveal repeating patterns of movement from the past.