Subsequence pattern discovery in Chinese university financial data

The financial data time series of a university contain important information of its resource allocation and developing trends, which can be revealed in their distinctive patterns. This work uses selective subsequence time series clustering based on motifs to discovery financial subsequence patterns in Chinese universities. The research finds out interesting patterns in eight university categories. The results can be used for guiding decision-making and forecasting the effects of decisions.