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Testing for Self-excitation in Financial Events: A Bayesian Approach.

, and . MIDAS/PAP@PKDD/ECML, volume 11054 of Lecture Notes in Computer Science, page 95-102. Springer, (2018)

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Testing for Self-excitation in Financial Events: A Bayesian Approach., and . MIDAS/PAP@PKDD/ECML, volume 11054 of Lecture Notes in Computer Science, page 95-102. Springer, (2018)An application of deep learning for trade signal prediction in financial markets., and . SIU, page 2521-2524. IEEE, (2015)AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting., , , , , , and . AutoML, volume 224 of Proceedings of Machine Learning Research, page 9/1-21. PMLR, (2023)FastPoint: Scalable Deep Point Processes., , and . ECML/PKDD (2), volume 11907 of Lecture Notes in Computer Science, page 465-480. Springer, (2019)Political interest and tendency prediction from microblog data., and . SIU, page 1327-1330. IEEE, (2014)Sentiment extraction from financial public disclosure documents.. MIDAS@PKDD/ECML, volume 1774 of CEUR Workshop Proceedings, page 67-72. CEUR-WS.org, (2016)Fast high-dimensional temporal point processes with applications (Hızlı yüksek boyutlu zamansal nokta süreçleri ve uygulamaları). Boğaziçi University, Turkey, (2020)Detecting Anomalous Event Sequences with Temporal Point Processes., , , , and . NeurIPS, page 13419-13431. (2021)Deep Explicit Duration Switching Models for Time Series., , , , , , , and . NeurIPS, page 29949-29961. (2021)Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies., , , , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 10534-10554. PMLR, (2022)