Coordination Event Detection and Initiator Identification in Time Series Data

TL;DR

This paper introduces a novel framework for detecting coordinated activity periods, identifying initiators, and classifying mechanisms in time series data, applicable to social animals and financial markets, with demonstrated effectiveness on real and simulated data.

Contribution

It presents the first computational approach for local leadership detection and coordination mechanism classification in time series data, advancing beyond existing global methods.

Findings

Successfully identified coordination events in animal trajectories and stock data.

Accurately detected initiators and classified mechanisms of coordination.

Found known biological events and unreflected financial events in data.

Abstract

Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the "Coordination Initiator Inference Problem" and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time-series data from interacting entities.