Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Probabilistic reasoning in decision support systems: from computation to common sense
Probabilistic reasoning in decision support systems: from computation to common sense
Communications of the ACM
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Approximate learning of dynamic models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Information Sciences: an International Journal
Context-specific sign-propagation in qualitative probabilistic networks
Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
A Data Model for Time-Series Analysis
Advanced Database Systems
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Reasoning and unsupervised learning in a fuzzy cognitive map
Information Sciences—Informatics and Computer Science: An International Journal
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Temporal Functional Dependencies and Temporal Nodes Bayesian Networks
The Computer Journal
Adaptive estimated maximum-entropy distribution model
Information Sciences: an International Journal
Using multiple indexes for efficient subsequence matching in time-series databases
Information Sciences: an International Journal
Qualitative reasoning about consistency in geographic information
Information Sciences: an International Journal
Introducing situational signs in qualitative probabilistic networks
International Journal of Approximate Reasoning
Swarm intelligent surfing in the web
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using qualitative relationships for bounding probability distributions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Robust observers for neutral jumping systems with uncertain information
Information Sciences: an International Journal
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discovering semantic associations among Web services based on the qualitative probabilistic network
Expert Systems with Applications: An International Journal
Qualitative probabilistic networks with reduced ambiguities
Applied Intelligence
Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Modeling and analysis of time-series data attract much attention in data mining and knowledge discovery community due to their many applications in financial analysis, automation control, etc. In such applications, time-series data usually contain several attributes that may be causally dependent in historical time slices. Dangerous feedback loops of attributes' dependent relationships can make the system collapse due to amplification or oscillation of attribute values. Motivated by efficient analysis of causalities in time-series data, we propose a temporal qualitative probabilistic graphical model in this paper. From given time-series sample data, we construct the structure of the temporal qualitative probabilistic network (TQPN) and derive the corresponding qualitative influences on directed edges. We then present the approach for TQPN reasoning with time-series features. Consequently, positive time-series feedback loops are defined, and the approach to identify them is proposed. Preliminary experiments show that our proposed method is not only feasible but also efficient.