An Approach of Time Series Piecewise Linear Representation Based on Local Maximum Minimum and Extremum
Changfeng Yan · Journal of Information and Computational Science · 2013
Time series is a kind of important complex data. It is a non-trivial problem to store data, analyze data and mine knowledge in its original data directly because of the inherent high dimensionality and complexity of the data. It is the most promising solution to achieve dimensionality reduction on the data. There are five major techniques in dimensionality reduction such as Discrete Fourier Transform, Discrete Wavelets Transform, Singular Value Decomposition, Symbolic Representation and Piecewise Linear Representation. Integrating the idea of important point and extreme point, a new approach of time series piecewise linear representation are proposed based on local maximum minimum and extremum in this paper. The results of experiments by using the public datasets from several different fields are shown that the proposed technique appears better fitting effect on the adjacent data value less volatile datasets and it has nice fitting effects in the volatile datasets under the low compression ratio condition compared with two other piecewise liner representation techniques.