A Novel Fractal Representation for Dimensionality Reduction of Large Time Series Data

Authors:
Poat Sajjipanon;Chotirat Ann Ratanamahatana
Affiliations:
Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand 10330;Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand 10330
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

Citing 6
Cited 0

Using the fractal dimension to cluster datasets

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A symbolic representation of time series, with implications for streaming algorithms

DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Chaos and Fractals

Chaos and Fractals
Towards parameter-free data mining

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping

Knowledge and Information Systems
Clustering Time Series with Clipped Data

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent research has attempted to speed up time series data mining tasks which focus on dimensionality reduction, indexing, and lower bounding function, among many others. For large time series data, current dimensionality reduction techniques cannot reduce the total dimensions of time series data by a large margin without losing their global characteristics. In this paper, we introduce a novel Fractal Representation which uses merely three real values to represent a whole time series data sequence. Moreover, our proposed representation can be efficiently used under Euclidean distance. We demonstrate effectiveness and utility of our novel Fractal Representation on classification problems and our proposed method outperforms existing methods in terms of speed performance and accuracy. Our results reconfirm that this representation can effectively represent global characteristics of the data, especially in larger time series data.