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 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 Feature Extraction of Sequence of Keystrokes in Fixed Text Using the Multivariate Hawkes Process

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chang Zhang ◽  
Yuchen Zhang ◽  
Fulin Li
Keyword(s):  
Feature Extraction ◽  
Process Model ◽  
Visual Analysis ◽  
Classification Performance ◽  
Intensity Function ◽  
Hawkes Process ◽  
Raw Data ◽  
Rich Information ◽  
One Class Classifier ◽  
Function Vector

In this paper, we propose a new method of extracting the features of keystrokes. The Hawkes process based on exponential excitation kernel was used to model the sequence of keystrokes in fixed text, and the intensity function vector and adjacency matrix of the model obtained through training were regarded as the characteristics of the keystrokes. A visual analysis was carried out on the CMU keystroke raw data and the feature data extracted using the proposed method. We used one-class classifier to compare the classification effect of CMU keystroke raw data and the feature data extracted by the Hawkes process model and POHMM model. The experimental results show that the feature data extracted using the proposed method contains rich information to distinguish users. In addition, the feature data extracted using the proposed method has a slightly better classification performance than the original CMU keystroke data for some users who are not easy to distinguish.

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