SIMILARITY ANALYSIS OF TIME SERIES DATA BY WISAM

• MORI Takakazu

  Faculty of Science, Kyoto Sangyo University

• MISAWA Tetsuya

  Faculty of Economics, Nagoya City University

Abstract

This article focuses on a new characteristic quantity, "similarity distance", which is defined for a pair of time series data and reflects similarity between them. This characteristic quantity is defined with the help of a smooth approximating function, which is obtained by "WISAM (Wavelet Interpolation Method with Simulated Annealing)" developed by Mori (1999) and Mori and Misawa (2001). Afterwards, as an illustrated example of the usage of similarity distance together with WISAM, the classifications and similarity of the annual GDP data for ten regions in Japan are investigated.

Journal

• Journal of the Japanese Society of Computational Statistics

  Journal of the Japanese Society of Computational Statistics 19 (1), 15-26, 2006-12-01

  Japanese Society of Computational Statistics

Keywords

• Least square method
• Meyer wavelet
• Similarity distance
• Simulated annealing

Details

• CRID: 1574231877046271744
• NII Article ID: 110006289219
• NII Book ID: AA10823693
• ISSN: 09152350
• Text Lang: en
• Data Source:
  • CiNii Articles