1 / 11

Multivariate Time Series Analysis for Early Problem Detection with Dynamic Bayesian Networks

This work explores the application of Dynamic Bayesian Networks (DBNs) for detecting early signs of problems in various fields, including oil refineries and gene expression studies. By utilizing multivariate time series data, the study addresses challenges such as large variable numbers, short time series lengths, and changing dependencies. Innovative efficient learning algorithms and architectures are developed, enabling non-statisticians to integrate expert knowledge with data. The approaches involve intelligent operators and heuristic grouping algorithms to enhance DBN performance, paving the way for future research and clinical applications.

tommy
Télécharger la présentation

Multivariate Time Series Analysis for Early Problem Detection with Dynamic Bayesian Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Explaining Multivariate Time Series to Detect Early Problem Signs Architectures and Efficient Learning Algorithms for Dynamic Bayesian Networks Allan Tucker, Xiaohui Liu

  2. Datasets • Visual Field & Gene Expression • Large/Huge number of variables • Short Multivariate Time Series • Longitudinal (Experimental Conditions / Patients) • Oil Refinery • Large Possible Time Lags • Changing Dependencies

  3. Dynamic Bayesian Networks • Probabilistic Graphical Models • Easily Used by Non-Statisticians • Able to Combine Expert Knowledge with Data • Incorporate Hidden / Temporal Nodes etc.

  4. Developing Specialist DBNs • Previously Used DBNs to Generate Explanations from Oil Refinery Data • Hidden Nodes to Model Changing Operating Modes • DBN Model to Combine Visual Field MTS Data with Non-MTS Clinical Data • Combining Gene Expression Experiments

  5. DBN Architectures

  6. Efficient Learning Algorithms • Heuristic Grouping Algorithms • Seeding Evolutionary Algorithms • Intelligent Operators • Time Lag Mutation Operators • DBN Link Crossover Operators • Spatial Crossover and Mutation (VF Data)

  7. Some Sample DBNs

  8. Explanations

  9. Explanations

  10. Sample of Publications A Tucker, S Swift and X Liu, "Variable Grouping in Multivariate Time Series via Correlation", IEEE Transactions on Systems, Man & Cybernetics: Part B: Cybernetics, 31:235-245, (2001). A Tucker, X Liu and A Ogden-Swift, “Evolutionary Learning of Dynamic Probabilistic Models with Large Time Lags”, International Journal of Intelligent Systems, 16:621-645, (2001). P Kellam, X Liu, N Martin, C Orengo, S Swift, A Tucker, “A Framework for Modelling Virus Gene Expression Data”, Intelligent Data Analysis – An International Journal, Vol. 6, No. 3, IOS Press, Netherlands, pp. 265-280, (2002).

  11. The Future • Extend Work on DBNs for VF Data • Incorporate Expert Knowledge • Include more clinical information • Classify types of disease from MTS • Look into Modelling Continuous Variables • Gaussian Networks • Continuous BNs

More Related