A study on time series data mining based on the concepts and principles of Chinese I-Ching

Abstract

This paper proposes a novel time series data mining and analysis framework inspired by ancient Chinese culture I-Ching. The proposed method converts the time series into symbol spaces by employing the concepts and principles of I-Ching. Algorithms are addressed to explore and identify temporal patterns in the resulting symbol spaces. Using the analysis framework, major topics of time series data mining regarding time series clustering, association rules of temporal patterns, and transition of hidden Markov process can be analyzed. Dynamic patterns are derived and adopted to investigate the occurrence of special events existing in the time series. A case study is illustrated to demonstrate the effectiveness and usefulness of the proposed analysis framework. Key words: Time series, data mining, Chinese I-Ching.