scholarly journals A non‐parametric Hawkes process model of primary and secondary accidents on a UK smart motorway

2020 ◽  
Author(s):  
Kieran Kalair ◽  
Colm Connaughton ◽  
Pierfrancesco Alaimo Di Loro
Keyword(s):  
Process Model ◽  
Hawkes Process ◽  
Non Parametric

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

scholarly journals Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liang ◽  
Zhengyi Shi ◽  
Feifei Zhu ◽  
Wenxin Chen ◽  
Xin Chen ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Probabilistic Model ◽  
Autoregressive Model ◽  
Process Model ◽  
Joint Angle ◽  
Gaussian Process Model ◽  
The Mean ◽  
Test Scenarios ◽  
Semg Signals ◽  
Non Parametric

There is uncertainty in the neuromusculoskeletal system, and deterministic models cannot describe this significant presence of uncertainty, affecting the accuracy of model predictions. In this paper, a knee joint angle prediction model based on surface electromyography (sEMG) signals is proposed. To address the instability of EMG signals and the uncertainty of the neuromusculoskeletal system, a non-parametric probabilistic model is developed using a Gaussian process model combined with the physiological properties of muscle activation. Since the neuromusculoskeletal system is a dynamic system, the Gaussian process model is further combined with a non-linear autoregressive with eXogenous inputs (NARX) model to create a Gaussian process autoregression model. In this paper, the normalized root mean square error (NRMSE) and the correlation coefficient (CC) are compared between the joint angle prediction results of the Gaussian process autoregressive model prediction and the actual joint angle under three test scenarios: speed-dependent, multi-speed and speed-independent. The mean of NRMSE and the mean of CC for all test scenarios in the healthy subjects dataset and the hemiplegic patients dataset outperform the results of the Gaussian process model, with significant differences (p < 0.05 and p < 0.05, p < 0.05 and p < 0.05). From the perspective of uncertainty, a non-parametric probabilistic model for joint angle prediction is established by using Gaussian process autoregressive model to achieve accurate prediction of human movement.

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.

SEMI-PARAMETRIC AND NON-PARAMETRIC MODELING OF THE TEMPORAL ACTIVITY OF THE HYPOTHALAMIC PULSE GENERATOR IN CASTRATE MONKEYS

1995 ◽  
Vol 03 (04) ◽  
pp. 1125-1129
Author(s):  
A.-C. CAMPROUX ◽  
J.-P. JAIS ◽  
J.-C. THALABARD ◽  
G. THOMAS
Keyword(s):  
Experimental Data ◽  
Process Model ◽  
Pulse Generator ◽  
Temporal Structure ◽  
Parametric Modeling ◽  
Parametric Approach ◽  
Stochastic Point Process ◽  
Periodic Regression ◽  
Ovariectomized Rhesus Monkey ◽  
Non Parametric

The luteinizing hormone (LH) is released by the pituitary in discrete pulses. Electro-physiological studies in monkeys have demonstrated that sharp intermittent increases in the electrical activity of a hypothalamic pulse generator (HPG) are associated in a one-to-one manner with the occurrence of LH pulses in the plasma and exhibits a circadian modulation. In order to investigate the temporal structure of the HPG, we develop a semi-parametric stochastic point process model generalizing the Cox's periodic regression model. We apply this approach to the study of memory range of the process underlying HPG activity, using experimental data from one ovariectomized rhesus monkey. A non-parametric approach is also described.

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.

scholarly journals Variational Inference for Sparse Gaussian Process Modulated Hawkes Process

2020 ◽  
Vol 34 (04) ◽  
pp. 6803-6810
Author(s):  
Rui Zhang ◽  
Christian Walder ◽  
Marian-Andrei Rizoiu
Keyword(s):  
Model Selection ◽  
Gaussian Process ◽  
Linear Time ◽  
Expectation Maximization Algorithm ◽  
Synthetic Data ◽  
Variational Inference ◽  
Hawkes Process ◽  
Prediction Confidence ◽  
Inference Schema ◽  
Non Parametric

The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models suffers from unscalability or slow convergence. In this paper, we aim to solve both problems. Specifically, first, we propose a new non-parametric Bayesian HP in which the triggering kernel is modeled as a squared sparse Gaussian process. Then, we propose a novel variational inference schema for model optimization. We employ the branching structure of the HP so that maximization of evidence lower bound (ELBO) is tractable by the expectation-maximization algorithm. We propose a tighter ELBO which improves the fitting performance. Further, we accelerate the novel variational inference schema to linear time complexity by leveraging the stationarity of the triggering kernel. Different from prior acceleration methods, ours enjoys higher efficiency. Finally, we exploit synthetic data and two large social media datasets to evaluate our method. We show that our approach outperforms state-of-the-art non-parametric frequentist and Bayesian methods. We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection. We observe that the tighter ELBO exceeds the common one in model selection.

scholarly journals Cognitive process modeling addresses context independence violations in the ABCD Study stop-signal task

2021 ◽  
Author(s):  
Alexander Weigard ◽  
Dora Matzke ◽  
Charlotte Tanis ◽  
Andrew Heathcote
Keyword(s):  
Middle Childhood ◽  
Cognitive Process ◽  
Process Model ◽  
Design Feature ◽  
Mechanistic Explanation ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Stop Signal Reaction Time ◽  
The Impact ◽  
Non Parametric

The Adolescent Brain Cognitive Development (ABCD) Study is a longitudinal neuroimaging study of unprecedented scale that is in the process of following over 11,000 youth from middle childhood though age 20. However, a design feature of the study's stop-signal task violates "context independence", an assumption critical to current non-parametric methods for estimating stop-signal reaction time (SSRT), a key measure of inhibitory ability in the study. This has led some experts to call for the task to be changed and for previously collected data to be used with caution. We present a formal cognitive process model, the BEESTS-ABCD model, that provides a mechanistic explanation for the impact of this design feature, describes key behavioral trends in the ABCD data, and allows biases in SSRT estimates resulting from context independence violations to be quantified. We use the model to demonstrate that, although non-parametric SSRT estimates generally preserve the rank ordering of participants' SSRT values, failing to account for context independence violations can lead to erroneous inferences in several realistic scenarios. Nonetheless, as the BEESTS-ABCD model can be used to accurately recover estimates of SSRT and other mechanistic parameters of interest from ABCD data, the impact of such violations can be effectively mitigated.

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.

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