Dimension Reduction for Clustering Time Series Using Global Characteristics

Existing methods for time series clustering rely on the actual data values can become impractical since the methods do not easily handle dataset with high dimensionality, missing value, or different lengths. In this paper, a dimension reduction method is proposed that replaces the raw data with some global measures of time series characteristics. These measures are then clustered using a self-organizing map. The proposed approach has been tested using benchmark time series previously reported for time series clustering, and is shown to yield useful and robust clustering.

• Type: Conference
• Year: 2005
• Where: ICCS
• Authors: Xiaozhe Wang, Kate A. Smith, Rob J. Hyndman
• Keywords: ICCS 2005, Time Series, Time Series Characteristics, Time Series Clustering