Discovering Fuzzy Partial Periodic Patterns in Quantitative Irregular Multiple Time Series
Pamalla Veena, Palla Likhitha, Rage Uday Kiran, José María Luna, Philippe Fournier‐Viger, Koji Zettsu · 2023
Partial periodic patterns are an important class of regularities in multiple time series data. Most previous works focused on finding these patterns in a binary series by disregarding the quantities of objects. This paper explores the concept of “fuzzy sets” and proposes a novel model of fuzzy partial periodic patterns (F3Ps) that may exist in a quantitative series. F3Ps have value because they represent regularities that are predictable in a series. Unfortunately, finding F3Ps is challenging due to its colossal search space. We introduce a novel pruning technique to reduce the search space and computational cost of finding these patterns. We also present an efficient depth-first search algorithm, F3P-Miner, to find all F3Ps in a series. We also describe a new pattern representation technique, temporal ordering and grouping with a wildcard character, to visualize long patterns easily. Experimental results demonstrate that the proposed algorithm is efficient. Finally, we present the real-world applicability of F3Ps by finding helpful information about polluted areas in the one year Japan's air pollution database.