More Web Proxy on the site http://driver.im/

research-article

Long Horizon Forecasting with Temporal Point Processes

Authors:

Prathamesh Deshpande,

Kamlesh Marathe,

Sunita SarawagiAuthors Info & Claims

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Pages 571 - 579

https://doi.org/10.1145/3437963.3441740

Published: 08 March 2021 Publication History

Abstract

In recent years, marked temporal point processes (MTPPs) have emerged as a powerful modeling machinery to characterize asynchronous events in a wide variety of applications. MTPPs have demonstrated significant potential in predicting event-timings, especially for events arriving in near future. However, due to current design choices, MTPPs often show poor predictive performance at forecasting event arrivals in distant future. To ameliorate this limitation, in this paper, we design DualTPP which is specifically well-suited to long horizon event forecasting. DualTPP has two components. The first component is an intensity free MTPP model, which captures microscopic event dynamics by modeling the time of future events. The second component takes a different dual perspective of modeling aggregated counts of events in a given time-window, thus encapsulating macroscopic event dynamics. Then we develop a novel inference framework jointly over the two models by solving a sequence of constrained quadratic optimization problems. Experiments with a diverse set of real datasets show that DualTPP outperforms existing MTPP methods on long horizon forecasting by substantial margins, achieving almost an order of magnitude reduction in Wasserstein distance between actual events and forecasts. The code and the datasets can be found at the following URL: https://github.com/pratham16cse/DualTPP

Supplementary Material

MP4 File (March 11_Session 14_1-Prathamesh Deshpande_577.mp4)

Presentation of our work on Long Horizon Forecasting With Temporal Point Processes

Download
42.18 MB

References

[1]

911 dataset. URL https://www.kaggle.com/mchirico/montcoalert.

[2]

Taxi dataset. URL https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page.

[3]

I. Apostolopoulou, S. Linderman, K. Miller, and A. Dubrawski. Mutually regressive point processes. In NeurIPS, pages 5115--5126, 2019.

[4]

S. Ben Taieb and A. Atiya. A bias and variance analysis for multistep-ahead time series forecasting. IEEE transactions on neural networks and learning systems, 27(3), 2015.

[5]

J. Bernardo, M. Bayarri, J. Berger, A. Dawid, D. Heckerman, A. Smith, and M. West. The markov modulated poisson process and markov poisson cascade with applications to web traffic modeling. Bayesian Statistics, 2003.

[6]

A. Borovykh, S. Bohte, and C. W. Oosterlee. Conditional time series forecasting with convolutional neural networks, 2017.

[7]

R. Cai, X. Bai, Z. Wang, Y. Shi, P. Sondhi, and H. Wang. Modeling sequential online interactive behaviors with temporal point process. In CIKM, pages 873--882, 2018.

Digital Library

[8]

A. De, S. Bhattacharya, and N. Ganguly. Demarcating endogenous and exogenous opinion diffusion process on social networks. In WWW, pages 549--558, 2018.

[9]

P. Deshpande and S. Sarawagi. Streaming adaptation of deep forecasting models using adaptive recurrent units. In ACM SIGKDD, 2019.

Digital Library

[10]

D. Deutsch, S. Upadhyay, and D. Roth. A general-purpose algorithm for constrained sequential inference. In M. Bansal and A. Villavicencio, editors, Proceedings of the 23rd Conference on Computational Natural Language Learning, CoNLL 2019, Hong Kong, China, November 3--4, 2019, pages 482--492. Association for Computational Linguistics, 2019.

[11]

N. Du, L. Song, M. Yuan, and A. J. Smola. Learning networks of heterogeneous influence. In NeurIPS, pages 2780--2788. Curran Associates, Inc., 2012.

[12]

N. Du, L. Song, H. Woo, and H. Zha. Uncover topic-sensitive information diffusion networks. In Artificial Intelligence and Statistics, pages 229--237, 2013.

[13]

N. Du, H. Dai, R. Trivedi, U. Upadhyay, M. Gomez-Rodriguez, and L. Song. Recurrent marked temporal point processes: Embedding event history to vector. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1555--1564, 2016.

Digital Library

[14]

E. Fersini, E. Messina, G. Felici, and D. Roth. Soft-constrained inference for named entity recognition. Inf. Process. Manag., 50 (5): 807--819, 2014.

Digital Library

[15]

V. Filimonov and D. Sornette. Apparent criticality and calibration issues in the hawkes self-excited point process model: application to high-frequency financial data. Quantitative Finance, 15 (8): 1293--1314, 2015.

[16]

V. Flunkert, D. Salinas, and J. Gasthaus. Deepar: Probabilistic forecasting with autoregressive recurrent networks. CoRR, abs/1704.04110, 2017.

[17]

K. Giesecke and G. Schwenkler. Filtered likelihood for point processes. Journal of Econometrics, 204 (1): 33--53, 2018.

[18]

R. Gupta, S. Sarawagi, and A. A. Diwan. Collective inference for extraction mrfs coupled with symmetric clique potentials. JMLR, 11, Nov. 2010.

[19]

ark(2019)]hambly2019spdeB. Hambly and A. Søjmark. An spde model for systemic risk with endogenous contagion. Finance and Stochastics, 23 (3): 535--594, 2019.

[20]

]hawkes1971pointA. G. Hawkes. Point spectra of some mutually exciting point processes. Journal of the Royal Statistical Society: Series B (Methodological), 33 (3): 438--443, 1971 a .

[21]

]hawkes1971spectraA. G. Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58 (1): 83--90, 1971 b .

[22]

A. G. Hawkes. Hawkes jump-diffusions and finance: a brief history and review. The European Journal of Finance, pages 1--15, 2020.

[23]

V. Isham and M. Westcott. A self-correcting point process. Stochastic processes and their applications, 8 (3): 335--347, 1979.

[24]

H. Jing and A. J. Smola. Neural survival recommender. In WSDM, pages 515--524, 2017.

Digital Library

[25]

P. Kohli, L. Ladicky, and P. H. S. Torr. Robust higher order potentials for enforcing label consistency. International Journal of Computer Vision, 82 (3): 302--324, 2009.

Digital Library

[26]

Q. Kong, M.-A. Rizoiu, and L. Xie. Modeling information cascades with self-exciting processes via generalized epidemic models. In WSDM, pages 286--294, 2020.

Digital Library

[27]

S. Lamprier. A recurrent neural cascade-based model for continuous-time diffusion. In ICML, volume 97, pages 3632--3641. PMLR, 2019.

[28]

V. LE GUEN and N. THOME. Shape and time distortion loss for training deep time series forecasting models. In Advances in Neural Information Processing Systems 32. 2019.

[29]

G. Loaiza-Ganem, S. Perkins, K. Schroeder, M. Churchland, and J. P. Cunningham. Deep random splines for point process intensity estimation of neural population data. In NeurIPS, pages 13346--13356, 2019.

[30]

a]maciak2019infinitelyM. Maciak, O. Okhrin, and M. Pevs ta. Infinitely stochastic micro forecasting. arXiv, pages arXiv--1908, 2019.

[31]

H. Mei and J. M. Eisner. The neural hawkes process: A neurally self-modulating multivariate point process. In NeurIPS, pages 6754--6764, 2017.

[32]

Y. Ogata. Space-time point-process models for earthquake occurrences. Annals of the Institute of Statistical Mathematics, 50 (2): 379--402, 1998.

[33]

M. Okawa, T. Iwata, T. Kurashima, Y. Tanaka, H. Toda, and N. Ueda. Deep mixture point processes: Spatio-temporal event prediction with rich contextual information. In KDD, pages 373--383, 2019.

Digital Library

[34]

T. Omi, K. Aihara, et al. Fully neural network based model for general temporal point processes. In NeurIPS, pages 2122--2132, 2019.

[35]

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 2002.

Digital Library

[36]

V. Punyakanok, D. Roth, W. Yih, and D. Zimak. Learning and inference over constrained output. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), pages 1124--1129, 2005.

Digital Library

[37]

Z. Qian, A. M. Alaa, A. Bellot, J. Rashbass, and M. van der Schaar. Learning dynamic and personalized comorbidity networks from event data using deep diffusion processes. arXiv preprint arXiv:2001.02585, 2020.

[38]

S. Ramalingam, P. Kohli, K. Alahari, and P. H. S. Torr. Exact inference in multi-label crfs with higher order cliques. In CVPR, 2008.

[39]

M.-A. Rizoiu and L. X. Xie. Online popularity under promotion: Viral potential, forecasting, and the economics of time. In Eleventh International AAAI Conference on Web and Social Media, 2017.

[40]

M.-A. Rizoiu, S. Mishra, Q. Kong, M. Carman, and L. Xie. Sir-hawkes: linking epidemic models and hawkes processes to model diffusions in finite populations. In WWW, pages 419--428, 2018.

[41]

A. Saichev and D. Sornette. Generating functions and stability study of multivariate self-excited epidemic processes. The European Physical Journal B, 83 (2): 271, 2011.

[42]

and Günnemann]shchur2019intensityO. Shchur, M. Bilovs, and S. Günnemann. Intensity-free learning of temporal point processes. arXiv preprint arXiv:1909.12127, 2019.

[43]

D. Tarlow, I. Givoni, and R. Zemel. Hop-map: Efficient message passing with high order potentials. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AI-STATS), volume 9, pages 812--819. JMLR: W&CP, 2010.

[44]

M. Trinh. Non-stationary processes and their application to financial high-frequency data. PhD thesis, University of Sussex, 2018.

[45]

U. Upadhyay, A. De, and M. G. Rodriguez. Deep reinforcement learning of marked temporal point processes. In NeurIPS, pages 3168--3178, 2018.

[46]

A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu. Wavenet: A generative model for raw audio. CoRR, abs/1609.03499, 2016.

[47]

et al.(2019)Vassøy, Ruocco, de Souza da Silva, and Aune]vassoy2019timeB. Vassøy, M. Ruocco, E. de Souza da Silva, and E. Aune. Time is of the essence: a joint hierarchical rnn and point process model for time and item predictions. In Web Search and Data Mining, pages 591--599, 2019.

[48]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin. Attention is all you need. In NIPS. 2017.

[49]

A. Venkatraman, M. Hebert, and J. Bagnell. Improving multi-step prediction of learned time series models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29, 2015.

[50]

R. Wen, K. Torkkola, and B. Narayanaswamy. A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053, 2017.

[51]

Xiao, Farajtabar, Ye, Yan, Song, and Zha]xiao2017wassersteinS. Xiao, M. Farajtabar, X. Ye, J. Yan, L. Song, and H. Zha. Wasserstein learning of deep generative point process models. In Advances in neural information processing systems, pages 3247--3257, 2017 a .

[52]

Xiao, Yan, Farajtabar, Song, Yang, and Zha]xiao2017jointS. Xiao, J. Yan, M. Farajtabar, L. Song, X. Yang, and H. Zha. Joint modeling of event sequence and time series with attentional twin recurrent neural networks. arXiv preprint arXiv:1703.08524, 2017 b .

[53]

Xiao, Yan, Yang, Zha, and Chu]xiao2017modelingS. Xiao, J. Yan, X. Yang, H. Zha, and S. M. Chu. Modeling the intensity function of point process via recurrent neural networks. In AAAI, 2017 c .

[54]

S. Xiao, H. Xu, J. Yan, M. Farajtabar, X. Yang, L. Song, and H. Zha. Learning conditional generative models for temporal point processes. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

[55]

S. Xiao, J. Yan, M. Farajtabar, L. Song, X. Yang, and H. Zha. Learning time series associated event sequences with recurrent point process networks. IEEE transactions on neural networks and learning systems, 30 (10): 3124--3136, 2019.

[56]

A. S. Yang. Modeling the Transmission Dynamics of Pertussis Using Recursive Point Process and SEIR model. PhD thesis, UCLA, 2019.

[57]

S.-H. Yang and H. Zha. Mixture of mutually exciting processes for viral diffusion. In International Conference on Machine Learning, pages 1--9, 2013.

Digital Library

[58]

Y. Zhong, B. Xu, G.-T. Zhou, L. Bornn, and G. Mori. Time perception machine: Temporal point processes for the when, where and what of activity prediction. arXiv preprint arXiv:1808.04063, 2018.

[59]

K. Zhou, H. Zha, and L. Song. Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes. In Artificial Intelligence and Statistics, pages 641--649, 2013.

[60]

S. Zuo, H. Jiang, Z. Li, T. Zhao, and H. Zha. Transformer hawkes process. arXiv preprint arXiv:2002.09291, 2020.

Cited By

Zhou WKang ZTian LZhang JLiu Y(2025)Non-autoregressive diffusion-based temporal point processes for continuous-time long-term event predictionExpert Systems with Applications10.1016/j.eswa.2024.126210267(126210)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126210
Gupta VBedathur S(2024)Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process FlowsACM Transactions on Intelligent Systems and Technology10.1145/3650045Online publication date: 29-Feb-2024
https://doi.org/10.1145/3650045
Boyd AChang YMandt SSmyth PEvans RShpitser I(2023)Inference for mark-censored temporal point processesProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625856(226-236)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625856
Show More Cited By

Index Terms

Long Horizon Forecasting with Temporal Point Processes

Recommendations

Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification
KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

The development of disastrous flood forecasting techniques able to provide warnings at a long lead-time (5-15 days) is of great importance to society. Extreme Flood is usually a consequence of a sequence of precipitation events occurring over from ...
Long-Term and Multi-Step Ahead Call Traffic Forecasting with Temporal Features Mining
Abstract
An accurate call traffic forecasting can help the call center to schedule and manage its employees more scientifically. Meanwhile, to meet the needs that some tasks in the call center require the prediction of call traffic in different time ...
Long-term forecasting of Internet backbone traffic

We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

March 2021

1192 pages

ISBN:9781450382977

DOI:10.1145/3437963

General Chairs:
Liane Lewin-Eytan
Amazon, Israel
,
David Carmel
Amazon, Israel
,
Elad Yom-Tov
Microsoft, Israel
,
Program Chairs:
Eugene Agichtein
Emory University and Amazon, USA
,
Evgeniy Gabrilovich
Google Health, USA

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM '21

Sponsor:

WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining

March 8 - 12, 2021

Virtual Event, Israel

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
370
Total Downloads

Downloads (Last 12 months)54
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhou WKang ZTian LZhang JLiu Y(2025)Non-autoregressive diffusion-based temporal point processes for continuous-time long-term event predictionExpert Systems with Applications10.1016/j.eswa.2024.126210267(126210)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126210
Gupta VBedathur S(2024)Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process FlowsACM Transactions on Intelligent Systems and Technology10.1145/3650045Online publication date: 29-Feb-2024
https://doi.org/10.1145/3650045
Boyd AChang YMandt SSmyth PEvans RShpitser I(2023)Inference for mark-censored temporal point processesProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625856(226-236)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625856
Li ZSun M(2023)Sparse Transformer Hawkes Process for Long Event SequencesMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43424-2_11(172-188)Online publication date: 18-Sep-2023
https://doi.org/10.1007/978-3-031-43424-2_11
Xue SShi XZhang JMei HKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)HYPROProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602780(34641-34650)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602780
Yu DChen JWang DXu YXiang ZDeng S(2022)DSIM: dynamic and static interest mining for sequential recommendationKnowledge and Information Systems10.1007/s10115-022-01715-364:8(2267-2288)Online publication date: 23-Jul-2022
https://doi.org/10.1007/s10115-022-01715-3
Bala Sundara Ganapathy NDeeptavarna M(2022)Artificial Cognition of Temporal Events Using Recurrent Point Process NetworksComputer Vision and Machine Intelligence Paradigms for SDGs10.1007/978-981-19-7169-3_9(95-102)Online publication date: 19-Nov-2022
https://doi.org/10.1007/978-981-19-7169-3_9
Gupta VBedathur SBhattacharya SDe A(undefined)Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point ProcessesACM Transactions on Intelligent Systems and Technology10.1145/3545118
https://dl.acm.org/doi/10.1145/3545118

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents