On the discovery of seasonal gradual patterns through periodic patterns mining

Abstract

Gradual patterns, capturing intricate attribute co-variations expressed as “when X increases/decreases, Y increases/decreases” in numerical data, play a vital role in managing vast volumes of complex numerical data in real-world applications. Recently, the data science community has focused on efficient extraction methods for gradual patterns from temporal data. However, there is a notable gap in approaches addressing the extraction of gradual patterns that capture seasonality from the graduality point of view in the temporal data sequences, despite their potential to yield valuable insights in applications such as e-commerce. This paper proposes a new method for extracting co-variations of periodically repeating attributes termed as seasonal gradual patterns. To achieve this, we formulate the task of mining seasonal gradual patterns as the problem of mining periodic patterns in multiple sequences and then, leverage periodic pattern mining algorithms to extract seasonal gradual patterns. Additionally, we propose a new antimonotonic support definition associated with these seasonal gradual patterns. Illustrative results from real-world datasets demonstrate the efficiency of the proposed approach and its ability to sift through numerous non-seasonal patterns to identify the seasonal ones.