Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Haikun Hong"'
Autor:
Sizhen Du, Haikun Hong
Publikováno v:
Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery ISBN: 9783030706647
Learning the latent dependence from time-series data is of great interest in network analysis, while most existing methods usually ignore the large volatility underlying the time series. In this paper, we develop a probabilistic model that leverages
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6874da53be3bf8d453a88de10cbb19d
https://doi.org/10.1007/978-3-030-70665-4_49
https://doi.org/10.1007/978-3-030-70665-4_49
Publikováno v:
Neurocomputing. 323:299-310
Temporal causal inference is a fundamental task in time series analysis and has attracted increasing attention in recent years. In many applications, we are presented with multiple target variables rather than a single one, and the relationships exis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::657f404d0e6bc43b19cd4c732e6e8043
https://doi.org/10.1016/j.neucom.2018.09.088
https://doi.org/10.1016/j.neucom.2018.09.088
Publikováno v:
Neural Computation. 30:271-291
Accurate causal inference among time series helps to better understand the interactive scheme behind the temporal variables. For time series analysis, an unavoidable issue is the existence of time lag among different temporal variables. That is, past
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::187ad1907de224d196b3dfd62bd32d4f
https://doi.org/10.1162/neco_a_01028
https://doi.org/10.1162/neco_a_01028
Publikováno v:
Neurocomputing. 259:76-84
Learning spatio-temporal dependency structure is meaningful to characterize causal or statistical relationships. In many real-world applications, dependency structure is often characterized by time-lag between variables. For example, traffic system a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::caaa8e28efc6399d1f52286f4e2a11cf
https://doi.org/10.1016/j.neucom.2016.06.084
https://doi.org/10.1016/j.neucom.2016.06.084
Publikováno v:
Science China Information Sciences. 61
Characterizing and understanding the structure and the evolution of networks is an important problem for many different fields.While in the real-world networks, especially the spatial networks, the influence from one node to another tends to vary ove
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e33b5f30ca4b9bb459b1b5b25f85fff
https://doi.org/10.1007/s11432-016-9070-4
https://doi.org/10.1007/s11432-016-9070-4
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 15:2191-2201
Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d329f11af5874efbf74427e739361e49
https://doi.org/10.1109/tits.2014.2311123
https://doi.org/10.1109/tits.2014.2311123
Publikováno v:
TAAI
The spatial clustering of highway traffic is of great interest to researchers and policy makers. In this paper, instead of using the microscopic traffic parameters in the traditional clustering methods, we introduce a new heterogeneity index clusteri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d039d5b84c46b41b25bf53bf325ec0f8
https://doi.org/10.1109/taai.2016.7880174
https://doi.org/10.1109/taai.2016.7880174
The shortest path or not? Analyzing the ambiguity of path selection in China's toll highway networks
Publikováno v:
ITSC
The highway toll road system in many countries is incapable of providing the detailed route information of users, and drivers may choose alternative paths rather than the shortest path in the hope of saving the travel cost. Existing ambiguous path id
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95fb53617c2454b6dc2d79827f4f246c
https://doi.org/10.1109/itsc.2016.7795873
https://doi.org/10.1109/itsc.2016.7795873
Autor:
Yu Lei, Xiabing Zhou, Xingxing Xing, Wenhao Huang, Kunqing Xie, Haikun Hong, Kaigui Bian, Fei Chen
Publikováno v:
ICMLA
Nearest neighbor based nonparametric regression is a classic data-driven method for traffic flow prediction in intelligent transportation systems (ITS). Performances of those models depend heavily on the similarity or distance metric used to search n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65e273bf0ce979627df2504fe4574d3c
https://doi.org/10.1109/icmla.2015.188
https://doi.org/10.1109/icmla.2015.188
Publikováno v:
ITSC
Research on traffic data analysis is becoming more available and important. One of the key challenges is how to accurately decompose the high-dimensional, noisy observation traffic flow matrix into sub-matrices that correspond to different classes of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0d14db66edd14c42a6c9bc04f7f978e
https://doi.org/10.1109/itsc.2015.358
https://doi.org/10.1109/itsc.2015.358