Research Area:  Data Mining

Abstract:

In most time series data mining, alternate forms of data representation or data preprocessing is required because of the unique characteristics of time series, such as high dimension (the number of data points), presence of random noise, and nonlinear relationship of the data elements. Therefore, any data representation method aims to achieve substantial data reduction to a manageable size, while preserving important characteristics of the original data, and robustness to random noise. Moreover, appropriate choice of a data representation method may result in meaningful data mining. Many high level representation methods of time series data are based on time domain approaches. These methods preprocess the original data in the time domain directly and are useful to understand the behavior of data over time. Piecewise approximation, data representation by identification important points, and symbolic representation are some of the main ideas of time domain approaches, and widely used in various fields.

Keywords:  
time series
data mining
time domai

Author(s) Name:  Seunghye J. Wilson

Publisher name:  Wiley

DOI:  10.1002/wics.1392

Volume Information:  Volume 9, Issue 1

Paper Link:   https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wics.1392