The statistical physics of discovering exogenous and endogenous factors in a chain of events
This provides a general tool for analyzing event time series in various domains, but it is incremental as it builds on existing statistical models.
The authors tackled the problem of distinguishing exogenous and endogenous factors in event sequences by developing a method that combines inhomogeneous Poisson and Hawkes processes, fitting it via free energy minimization, and applied it to social media data, showing estimated factors align with first and follow-up comments.
Event occurrence is not only subject to the environmental changes, but is also facilitated by the events that have occurred in a system. Here, we develop a method for estimating such extrinsic and intrinsic factors from a single series of event-occurrence times. The analysis is performed using a model that combines the inhomogeneous Poisson process and the Hawkes process, which represent exogenous fluctuations and endogenous chain-reaction mechanisms, respectively. The model is fit to a given dataset by minimizing the free energy, for which statistical physics and a path-integral method are utilized. Because the process of event occurrence is stochastic, parameter estimation is inevitably accompanied by errors, and it can ultimately occur that exogenous and endogenous factors cannot be captured even with the best estimator. We obtained four regimes categorized according to whether respective factors are detected. By applying the analytical method to real time series of debate in a social-networking service, we have observed that the estimated exogenous and endogenous factors are close to the first comments and the follow-up comments, respectively. This method is general and applicable to a variety of data, and we have provided an application program, by which anyone can analyze any series of event times.