Pattern recognition in time series database: A case study on financial database
Today, there are more and more time series data that coexist with other data. These data exist in useful and understandable patterns. Data management of time series data must take into account an integrated approach. However, many researches face numeric data attributes. Therefore, the need for time series data mining tool has become extremely important. The purpose of this paper is to provide a novel pattern in mining architecture with mixed attributes that uses a systematic approach in the financial database information mining. Time series pattern mining (TSPM) architecture combines the extended visualization-induced self-organizing map algorithm and the extended Nave Bayesian algorithm. This mining architecture can simulate human intelligence and discover patterns automatically. The TSPM approach also demonstrates good returns in pattern research.
data mining self-organizing map time series analysis portfolio strategy cluster analysis classification analysis