2013 ModelbasedKernelforEfficientTim

• (Chen et al., 2013) ⇒ Huanhuan Chen, Fengzhen Tang, Peter Tino, and Xin Yao. (2013). “Model-based Kernel for Efficient Time Series Analysis.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487700

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Model-based+Kernel+for+Efficient+Time+Series+Analysis
• http://dl.acm.org/citation.cfm?id=2487575.2487700&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; kernel methods; reservoir computing; time series; time series analysis

Abstract

We present novel, efficient, model based kernels for time series data rooted in the reservoir computation framework. The kernels are implemented by fitting reservoir models sharing the same fixed deterministically constructed state transition part to individual time series. The proposed kernels can naturally handle time series of different length without the need to specify a parametric model class for the time series. Compared with most time series kernels, our kernels are computationally efficient. We show how the model distances used in the kernel can be calculated analytically or efficiently estimated. The experimental results on synthetic and benchmark time series classification tasks confirm the efficiency of the proposed kernel in terms of both generalization accuracy and computational speed. This paper also investigates on-line reservoir kernel construction for extremely long time series.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 ModelbasedKernelforEfficientTim	Huanhuan Chen Fengzhen Tang Peter Tino Xin Yao			Model-based Kernel for Efficient Time Series Analysis				10.1145/2487575.2487700		2013