A Latent Hawkes Process Model for Event Clustering and Temporal Dynamics Learning with Applications in GitHub

2019 ◽  
Author(s):  
Shengzhong Liu ◽  
Shuochao Yao ◽  
Dongxin Liu ◽  
Huajie Shao ◽  
Yiran Zhao ◽  
...  
Keyword(s):  
Process Model ◽  
Temporal Dynamics ◽  
Hawkes Process ◽  
Event Clustering

scholarly journals Predicting Water Pipe Failures with a Recurrent Neural Hawkes Process Model

2020 ◽  
Author(s):  
Jeroen Verheugd ◽  
Paulo R de Oliveira da Costa ◽  
Reza Refaei Afshar ◽  
Yingqian Zhang ◽  
Sjoerd Boersma
Keyword(s):  
Process Model ◽  
Hawkes Process ◽  
Water Pipe

Multivariate Hawkes process model of market participants behavior in the high frequency world

2021 ◽  
Vol 08 (01) ◽  
pp. 2050054
Author(s):  
Sugato Chakravarty ◽  
Kiseop Lee ◽  
Yang Xi
Keyword(s):  
Public Sector ◽  
High Frequency ◽  
Process Model ◽  
Hawkes Process ◽  
Transaction Data ◽  
The Public ◽  
Market Participants ◽  
Public Sector Banks ◽  
Average Return ◽  
Causality Relationship

We propose a multivariate Hawkes process to model the interaction between the non-high frequency traders (NHFTs) behavior (Buy and sell) and high frequency traders (HFTs) behavior (Buy and sell). We apply our model to the intraday transaction data of the public sector banks stock in India, which is sampled from March 2012 to June 2012. We find that the mutually-exciting NHFT and HFT behaviors benefit the stocks, which have better average return above the average return of the public sector bank index. We further identify the granger causality relationship for mutually exciting dominating stocks that HFTs activities cause the activities of NHFTs. In other words, NHFTs are market followers in those stocks.

scholarly journals Spatio-temporal patterns of IED usage by the Provisional Irish Republican Army

2016 ◽  
Vol 27 (3) ◽  
pp. 377-402 ◽  
Author(s):  
STEPHEN TENCH ◽  
HANNAH FRY ◽  
PAUL GILL
Keyword(s):  
Process Model ◽  
Sociological Theory ◽  
Hawkes Process ◽  
Irish Republican Army ◽  
Improvised Explosive Device ◽  
The Troubles ◽  
Novel Approach ◽  
Explosive Device ◽  
Spatio Temporal ◽  
Improvised Explosive

In this paper, a unique dataset of improvised explosive device attacks during “The Troubles” in Northern Ireland (NI) is analysed via a Hawkes process model. It is found that this past dependent model is a good fit to improvised explosive device attacks yielding key insights about the nature of terrorism in NI. We also present a novel approach to quantitatively investigate some of the sociological theory surrounding the Provisional Irish Republican Army which challenges previously held assumptions concerning changes seen in the organisation. Finally, we extend our use of the Hawkes process model by considering a multidimensional version which permits both self and mutual-excitations. This allows us to test how the Provisional Irish Republican Army responded to past improvised explosive device attacks on different geographical scales from which we find evidence for the autonomy of the organisation over the six counties of NI and Belfast. By incorporating a second dataset concerning British Security Force (BSF) interventions, the multidimensional model allows us to test counter-terrorism (CT) operations in NI where we find subsequent increases in violence.

Self-exciting point process models for political conflict forecasting

2017 ◽  
Vol 29 (4) ◽  
pp. 685-707 ◽  
Author(s):  
N. JOHNSON ◽  
A. HITCHMAN ◽  
D. PHAN ◽  
L. SMITH
Keyword(s):  
Point Process ◽  
Early Warning ◽  
Early Warning System ◽  
Process Model ◽  
Warning System ◽  
Political Conflict ◽  
Process Models ◽  
Hawkes Process ◽  
Point Process Models ◽  
Using Data

In 2008, the Defense Advanced Research Project Agency commissioned a database known as the Integrated Crisis Early Warning System to serve as the foundation for models capable of detecting and predicting increases in political conflict worldwide. Such models, by signalling expected increases in political conflict, would help inform and prepare policymakers to react accordingly to conflict proliferation both domestically and internationally. Using data from the Integrated Crisis Early Warning System, we construct and test a self-exciting point process, or Hawkes process, model to describe and predict amounts of domestic, political conflict; we focus on Colombia and Venezuela as examples for this model. By comparing the accuracy of fitted models to the observed data, we find that we are able to closely describe occurrences of conflict in each country. Thus, using this model can allow policymakers to anticipate relative increases in the amount of domestic political conflict following major events.

scholarly journals Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates

2020 ◽  
Author(s):  
Wen-Hao Chiang ◽  
Xueying Liu ◽  
George Mohler
Keyword(s):  
Reproduction Number ◽  
Leading Indicators ◽  
Hawkes Process ◽  
Conditional Intensity ◽  
Short Term ◽  
Hawkes Processes ◽  
Event Clustering ◽  
Short Term Forecasting ◽  
Demographic Covariates ◽  
The U.S

AbstractHawkes processes are used in machine learning for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices and demographic covariates in the maximization step. We validate the approach on short-term forecasting tasks, showing that the Hawkes process outperforms several benchmark models currently used to track the pandemic, including an ensemble approach and a SEIR-variant. We also investigate which covariates and mobility indices are most important for building forecasts of COVID-19 in the U.S.

scholarly journals A Hawkes process model for the propagation of COVID-19: simple analytical results

2021 ◽  
Author(s):  
Juan Valentin Escobar
Keyword(s):  
Clinical Trials ◽  
Exponential Function ◽  
Process Model ◽  
Branching Process ◽  
Hawkes Process ◽  
Memory Kernel ◽  
Analytical Results ◽  
Analytical Expressions

Abstract We present a model for the COVID-19 epidemic that offers analytical expressions for the newly registered and latent cases. This model is based on an epidemic branching process with latency that is greatly simplified when the bare memory kernel is given by an exponential function as observed in this pandemic. We expose the futility of the concept of “bending the curve” of the epidemic as long as the number of latent cases is not depleted. Our model offers the possibility of laying out different scenarios for the evolution of the epidemic in different countries based on the most recent observations and in terms of only two constants obtained from clinical trials.

scholarly journals Temporal Heterogeneous Information Network Embedding

2021 ◽  
Author(s):  
Hong Huang ◽  
Ruize Shi ◽  
Wei Zhou ◽  
Xiao Wang ◽  
Hai Jin ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Information Network ◽  
Hawkes Process ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Meta Path ◽  
Low Dimensional ◽  
Type Node ◽  
Link Recommendation ◽  
Node Embeddings

Heterogeneous information network (HIN) embedding, learning the low-dimensional representation of multi-type nodes, has been applied widely and achieved excellent performance. However, most of the previous works focus more on static heterogeneous networks or learning node embedding within specific snapshots, and seldom attention has been paid to the whole evolution process and capturing all temporal dynamics. In order to fill the gap of obtaining multi-type node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). THINE not only uses attention mechanism and meta-path to preserve structures and semantics in HIN but also combines the Hawkes process to simulate the evolution of the temporal network. Our extensive evaluations with various real-world temporal HINs demonstrate that THINE achieves state-of-the-art performance in both static and dynamic tasks, including node classification, link prediction, and temporal link recommendation.

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