Time series classification based on spectral analysis

Shuen-Lin Jeng, Ya Ti Huang

研究成果: Article同行評審

10 引文 斯高帕斯(Scopus)


For time series data with obvious periodicity (e.g., electric motor systems and cardiac monitor) or vague periodicity (e.g., earthquake and explosion, speech, and stock data), frequency-based techniques using the spectral analysis can usually capture the features of the series. By this approach, we are able not only to reduce the data dimensions into frequency domain but also utilize these frequencies by general classification methods such as linear discriminant analysis (LDA) and k-nearest-neighbor (KNN) to classify the time series. This is a combination of two classical approaches. However, there is a difficulty in using LDA and KNN in frequency domain due to excessive dimensions of data. We overcome the obstacle by using Singular Value Decomposition to select essential frequencies. Two data sets are used to illustrate our approach. The classification error rates of our simple approach are comparable to those of several more complicated methods.

頁(從 - 到)132-142
期刊Communications in Statistics: Simulation and Computation
出版狀態Published - 2008 一月 1

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬


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