Behavior Algorithm — a Novel Time-series Clustering Approach

Abstract

Clustering time-series values is an established technique for organizations employing machine learning to analyze temporal datasets. Generally speaking, the goal of time-series methodology is to generate predictions. Such predictions could help organizations understand potential future cyberattacks, financial market changes, weather, or disease outbreaks. However, computational limitations lead existing algorithms to fail to group individual series together based the actual behavior of the series. A feature that can be used or derived to explain the time-series behavior had not been identified in the literature despite there being a need to have numeric values to describe the pattern of values over time. To address this gap, this work presents a behavior algorithm which addresses clustering time-series data based solely on the behavior of the series. Further, the algorithm is designed to operate effectively regardless of absolute values or temporal shifts. First, we describe the algorithm through mathematical examples. We provide the design approach for the algorithm numerically and through data visualizations. Then, we validated the algorithm on sample random data. Finally, we offer conclusions along with notions for future work based on this study.