A Novel Three-Way Deep Learning Approach for Multigranularity Fuzzy Association Analysis of Time Series Data

Discovering valuable knowledge from massive time series data is challenging due to sophisticated temporal relationships and inherent uncertainties. This paper proposes a new multigranularity fuzzy association analysis of multigranularity incorporated with three-way decisions inspired by humans. In particular, different fuzzy association rules are first mined at multiple time granularities. A three-way decision model is then designed to evaluate the credibility of each rule as a "positive", "negative", or "unknown" correlation. Further, we propose a novel deep learning approach to integrate the three-way fuzzy decisions across different granularities. By integrating the three-way decisions, more comprehensive and reliable fuzzy association knowledge can be obtained from Big Data from time series at different granularities, achieving more comprehensive and reliable discoveries than existing techniques. Extensive experiments demonstrate significant performance gains in real-world data sets from various domains. The synergistic integration of multigranularity mining, three-way decisions, and deep learning underpins a new problem solving paradigm to advance temporal knowledge discovery.