Clustering Then Estimation of Spatio-Temporal Self-Exciting Processes
Published Online:5 Sep 2024https://doi.org/10.1287/ijoc.2022.0351
References
- (2019) A new approach to real-time bidding in online advertisements: Auto pricing strategy. INFORMS J. Comput. 31(1):66–82.Link, Google Scholar
- (2023) Feature misspecification in sequential learning problems. Research paper, Columbia Business School, New York.Google Scholar
- (2012) Application of branching models in the study of invasive species. J. Amer. Statist. Assoc. 107(498):467–476.Crossref, Google Scholar
- (2021) How to assign scarce resources without money: Designing information systems that are efficient, truthful, and (pretty) fair. Inform. Systems Res. 32(2):335–355.Link, Google Scholar
- (2007) ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data Knowl. Eng. 60(1):208–221.Crossref, Google Scholar
- (2006) Budget-constrained, capacitated hub location to maximize expected demand coverage in fixed-wireless telecommunication networks. INFORMS J. Comput. 18(4):422–432.Link, Google Scholar
- (2011) A cluster-based context-tree model for multivariate data streams with applications to anomaly detection. INFORMS J. Comput. 23(3):364–376.Link, Google Scholar
- (2005) Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100(469):36–50.Crossref, Google Scholar
- (2021) Perfect sampling of Hawkes processes and queues with Hawkes arrivals. Stoch. Syst. 11(13):264–283.Link, Google Scholar
- (2014) American option sensitivities estimation via a generalized infinitesimal perturbation analysis approach. Oper. Res. 62(3):616–632.Link, Google Scholar
- (2019) Mise-optimal intervals for MNO–PQRS estimators of Poisson rate functions. 2019 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 368–379.Google Scholar
- (2022) On cluster-aware supervised learning: Frameworks, convergent algorithms, and applications. INFORMS J. Comput. 34(1):481–502.Link, Google Scholar
- (2019) Super-resolution estimation of cyclic arrival rates. Ann. Statist. 47(3):1754–1775.Crossref, Google Scholar
- Chen N, Gürlek R, Lee DK, Shen H (2024) Can customer arrival rates be modelled by sine waves? Service Sci. 16(2):70–84.Google Scholar
- (2008) Robust dynamic classes revealed by measuring the response function of a social system. Proc. Natl. Acad. Sci. USA 105(41):15649–15653.Crossref, Google Scholar
- (2010) Reliable facility location design under the risk of disruptions. Oper. Res. 58(4-part-1):998–1011.Google Scholar
- (2003) An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods (Springer, New York), 211–275.Google Scholar
- (2007) An Introduction to the Theory of Point Processes: Volume II: General Theory and Structure (Springer Science & Business Media, New York).Google Scholar
- (2018) Queues driven by Hawkes processes. Stoch. Syst. 8(3):192–229.Link, Google Scholar
- (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Series B (Methodological) 39(1):1–38.Google Scholar
- (2006) Spatio-temporal point processes: Methods and applications. Monogr. Statist. Appl. Probab. 107:1–46.Google Scholar
- Dong Z, Zhu S, Xie Y, Mateu J, Rodríguez-Cortés FJ (2023) Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data. J. Royal Statist. Soc. Ser. C App. Statist. 72(2):368–386.Google Scholar
- (2015) Dirichlet-Hawkes processes with applications to clustering continuous-time document streams. Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 219–228.Google Scholar
- (2016) Recurrent marked temporal point processes: Embedding event history to vector. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1555–1564.Google Scholar
- (2010) Affine point processes and portfolio credit risk. SIAM J. Financial Math. 1(1):642–665.Crossref, Google Scholar
- Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Second Internat. Conf. Knowledge Discovery Data Mining 96(34):226–231.Google Scholar
- (2017) Collaboration process pattern approach to improving teamwork performance: A data mining-based methodology. INFORMS J. Comput. 29(3):438–456.Link, Google Scholar
- (2017) Coevolve: A joint point process model for information diffusion and network co-evolution. Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 28 (Curran Associates, Inc., Red Hook, NY).Google Scholar
- (2015) The Hawkes process with different exciting functions and its asymptotic behavior. J. Appl. Probab. 52(1):37–54.Crossref, Google Scholar
- (2012) Quantifying reflexivity in financial markets: Toward a prediction of flash crashes. Phys. Rev. E 85(5):056108.Crossref, Google Scholar
- (2021) Nonparametric spatiotemporal analysis of violent crime. A case study in the Rio de Janeiro metropolitan area. Spat. Stat. 42:100431.Crossref, Google Scholar
- Gan G, Ma C, Wu J (2020) Data Clustering: Theory, Algorithms, and Applications (Society for Industrial and Applied Mathematics, Philadelphia).Google Scholar
- (2018) Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues. Queueing Syst. 90:161–206.Crossref, Google Scholar
- (1995) The query clustering problem: A set partitioning approach. IEEE Trans. Knowledge Data Eng. 7(6):885–899.Crossref, Google Scholar
- (2019) Optimal management of virtual infrastructures under flexible cloud service agreements. Inform. Systems Res. 30(4):1424–1446.Link, Google Scholar
- (2020) Scalable, adaptable, and fast estimation of transient downtime in virtual infrastructures using convex decomposition and sample path randomization. INFORMS J. Comput. 32(2):321–345.Abstract, Google Scholar
- (1971) Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1):83–90.Crossref, Google Scholar
- (1974) A cluster process representation of a self-exciting process. J. Appl. Probab. 11(3):493–503.Crossref, Google Scholar
- (2003) Estimation for nonhomogeneous Poisson processes from aggregated data. Oper. Res. Lett. 31(5):375–382.Crossref, Google Scholar
- (2018) Dirichlet process mixture models for modeling and generating synthetic versions of nested categorical data. Bayesian Anal. 13(1):183–200.Crossref, Google Scholar
- (2015) An introduction to simulation optimization. 2015 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 1780–1794.Google Scholar
- (2016) Principal component analysis: A review and recent developments. Philos. Trans. Roy. Soc. A 374(2065):20150202.Crossref, Google Scholar
- (2014a) Are call center and hospital arrivals well modeled by nonhomogeneous Poisson processes? Manufacturing Service Oper. Management 16(3):464–480.Link, Google Scholar
- (2014b) Choosing arrival process models for service systems: Tests of a nonhomogeneous Poisson process. Naval Res. Logist. 61(1):66–90.Crossref, Google Scholar
- (1990) The self-organizing map. Proc. IEEE 78(9):1464–1480.Crossref, Google Scholar
- (2001) Modeling and simulating Poisson processes having trends or nontrigonometric cyclic effects. European J. Oper. Res. 133(3):566–582.Crossref, Google Scholar
- (1991) Modeling and simulation of a nonhomogeneous Poisson process having cyclic behavior. Comm. Statist. Simulation Comput. 20(2–3):777–809.Crossref, Google Scholar
- Li H, Li H, Bhowmick SS (2020) Brunch: Branching structure inference of hybrid multivariate Hawkes processes with application to social media. Adv. Knowledge Discovery Data Mining: 24th Pacific-Asia Conf., PAKDD 2020, Proc., vol. 24, Part I (Springer International Publishing, New York), 553–566.Google Scholar
- (2019) Nonparametric method for modeling clustering phenomena in emergency calls under spatial-temporal self-exciting point processes. IEEE Access 7:24865–24876.Crossref, Google Scholar
- (2016) Personalized influential topic search via social network summarization. IEEE Trans. Knowledge Data Eng. 28(7):1820–1834.Crossref, Google Scholar
- (2018) Learning temporal point processes via reinforcement learning. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 10804–10814.Google Scholar
- (2023) Hawkes processes modeling, inference, and control: An overview. SIAM Rev. 65(2):331–374.Crossref, Google Scholar
- Lin SB, Tang S, Wang Y, Wang D (2022) Toward efficient ensemble learning with structure constraints: Convergent algorithms and applications. INFORMS J. Comput. 34(6):3096–3116.Google Scholar
- (2019b) Thread structure learning on online health forums with partially labeled data. IEEE Trans. Comput. Soc. Syst. 6(6):1273–1282.Crossref, Google Scholar
- (2018) Exploiting graph regularized multi-dimensional Hawkes processes for modeling events with spatio-temporal characteristics. IJCAI (AAAI Press, Palo Alto, CA), 2475–2482.Google Scholar
- (2019a) Modeling and simulation of nonstationary non-Poisson arrival processes. INFORMS J. Comput. 31(2):347–366.Link, Google Scholar
- (2018) Bayesian non-parametric generation of fully synthetic multivariate categorical data in the presence of structural zeros. J. Roy. Statist. Soc. Ser. A 181(3):635–647.Crossref, Google Scholar
- (2014) On the Hawkes process with different exciting functions. Preprint, submitted March 5, https://arxiv.org/abs/1403.0994.Google Scholar
- (2017) The neural Hawkes process: A neurally self-modulating multivariate point process. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 6757–6767.Google Scholar
- (2022) Fine-grained job salary benchmarking with a nonparametric Dirichlet process–based latent factor model. INFORMS J. Comput. 34(5):2443–2463.Link, Google Scholar
- (2014) Marked point process hotspot maps for homicide and gun crime prediction in Chicago. Internat. J. Forecast. 30(3):491–497.Crossref, Google Scholar
- (2011) Self-exciting point process modeling of crime. J. Amer. Statist. Assoc. 106(493):100–108.Crossref, Google Scholar
- (2019) A spline-based method for modelling and generating a nonhomogeneous Poisson process. 2019 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 356–367.Google Scholar
- (2020) The ease of fitting but futility of testing a nonstationary Poisson processes from one sample path. 2020 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 266–276.Google Scholar
- (2016) Hierarchical Clustering. Introduction to HPC with MPI for Data Science (Springer, New York), 195–211.Crossref, Google Scholar
- (1998) Space-time point-process models for earthquake occurrences. Ann. Inst. Statist. Math. 50(2):379–402.Crossref, Google Scholar
- (1979) Maximum likelihood estimation of Hawkes’ self-exciting point processes. Ann. Inst. Statist. Math. 31(1):145–155.Crossref, Google Scholar
- (2010) Generating Homogeneous Poisson Processes, Wiley Encyclopedia of Operations Research and Management Science (John Wiley, Hoboken, NJ).Google Scholar
- (1996) Asymptotic properties of the maximum likelihood estimator for spatio-temporal point processes. J. Statist. Plann. Inference 51(1):55–74.Crossref, Google Scholar
- (2018) A review of self-exciting spatio-temporal point processes and their applications. Statist. Sci. 33(3):299–318.Google Scholar
- (1992) Adventures in Stochastic Processes (Birkhäuser, Boston).Google Scholar
- Rizoiu MA, Lee Y, Mishra S, Xie L (2017) Hawkes processes for events in social media. Frontiers of Multimedia Research (Association for Computing Machinery and Morgan & Claypool, New York), 191–218.Google Scholar
- (2012) Simulating multivariate nonhomogeneous Poisson processes using projections. ACM Trans. Model. Comput. Simul. 22(3):1–13.Crossref, Google Scholar
- (2005) Consistent parametric estimation of the intensity of a spatial-temporal point process. J. Statist. Plann. Inference 128(1):79–93.Crossref, Google Scholar
- (2017) DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42(3):1–21.Crossref, Google Scholar
- (2014) Mathematical programming formulations and algorithms for discrete k-median clustering of time-series data. INFORMS J. Comput. 26(1):160–172.Link, Google Scholar
- (2010) Incremental information extraction using relational databases. IEEE Trans. Knowledge Data Eng. 24(1):86–99.Crossref, Google Scholar
- (2019) Real-time radiation treatment planning with optimality guarantees via cluster and bound methods. INFORMS J. Comput. 31(3):544–558.Link, Google Scholar
- (2008) Estimation of space–time branching process models in seismology using an EM–type algorithm. J. Amer. Statist. Assoc. 103(482):614–624.Crossref, Google Scholar
- (2022) A multilevel simulation optimization approach for quantile functions. INFORMS J. Comput. 34(1):569–585.Link, Google Scholar
- (2017b) Modeling the intensity function of point process via recurrent neural networks. Proc. AAAI Conf. Artificial Intelligence 31(1).Google Scholar
- (2017a) Wasserstein learning of deep generative point process models. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 3250–3259.Google Scholar
- (2018) Learning conditional generative models for temporal point processes. Proc. AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), vol. 32.Google Scholar
- (2017) A Dirichlet mixture model of Hawkes processes for event sequence clustering. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY).Google Scholar
- (2022) Online detection of supply chain network disruptions using sequential change-point detection for Hawkes processes. Preprint, submitted November 22, https://arxiv.org/abs/2211.12091.Google Scholar
- (2013) Mixture of mutually exciting processes for viral diffusion. Internat. Conf. Machine Learn. (PMLR, New York), 1–9.Google Scholar
- (2019) Multivariate spatiotemporal Hawkes processes and network reconstruction. SIAM J. Math. Data Sci. 1(2):356–382.Crossref, Google Scholar
- (1989) Simple fast algorithms for the editing distance between trees and related problems. SIAM J. Comput. 18(6):1245–1262.Crossref, Google Scholar
- (2020) Simulating nonstationary spatio-temporal Poisson processes using the inversion method. 2020 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 492–503.Google Scholar
- (2014) Scaling and modeling of call center arrivals. Proc. Winter Simulation Conf. 2014 (IEEE, Piscataway, NJ), 476–485.Google Scholar
- (2024) Clustering then estimation of spatio-temporal self-exciting processes. Accessed June 29, 2024, https://github.com/INFORMSJoC/2022.0351.Google Scholar
- (2017) Fitting continuous piecewise linear Poisson intensities via maximum likelihood and least squares. 2017 Winter Simulation Conference (WSC) (IEEE, Piscataway, NJ), 1740–1749.Google Scholar
- (2013a) Learning Social Infectivity in Sparse Low-Rank Networks Using Multi-Dimensional Hawkes Processes. Artificial Intelligence and Statistics (PMLR, New York), 641–649.Google Scholar
- (2013b) Learning triggering kernels for multi-dimensional Hawkes processes. Internat. Conf. Machine Learn. (PMLR, New York), 1301–1309.Google Scholar
- (2015) A spatio-temporal point process model for ambulance demand. J. Amer. Statist. Assoc. 110(509):6–15.Crossref, Google Scholar
- (2022) Spatiotemporal-textual point processes for crime linkage detection. Ann. Appl. Stat. 16(2):1151–1170.Crossref, Google Scholar
- Zhu S, Li S, Peng Z, Xie Y (2021a) Imitation learning of neural spatio-temporal point processes. IEEE Trans. Knowledge Data Eng. 34(11):5391–5402.Google Scholar
- (2020) Spatio-temporal point processes with attention for traffic congestion event modeling. Preprint, submitted May 15, https://arxiv.org/abs/2005.08665.Google Scholar
- Zhu S, Yao R, Xie Y, Qiu F, Wu X (2021b) Quantifying grid resilience against extreme weather using large-scale customer power outage data. Preprint, submitted September 20, https://arxiv.org/abs/2109.09711.Google Scholar
- (2002) Stochastic declustering of space-time earthquake occurrences. J. Amer. Statist. Assoc. 97(458):369–380.Crossref, Google Scholar
- (2004) Analyzing earthquake clustering features by using stochastic reconstruction. J. Geophys. Res. Solid Earth 109(B5):1–17.Crossref, Google Scholar
- (2016) Point-process models of social network interactions: Parameter estimation and missing data recovery. Eur. J. Appl. Math. 27(3):502–529.Crossref, Google Scholar

