Fast Algorithms for Mining Co-evolving Time Series
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Summary
This thesis will present special models and algorithms for learning time series models, in particular, including Linear Dynamical Systems (LDS) and Hidden Markov Models (HMM), and develop a distributed algorithm for finding patterns in large web-click streams.
- Type
- article
- Published
- 2011-01-01
- Cited by
- 9
- References
- 165
- Access
- Open access
- OpenAlex
- https://openalex.org/W82592655
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59830558
Keywords
Computer science, Automatic summarization, Hidden Markov model, Data mining, Dynamic time warping
References
- Forecasting structural time series models and the kalman filter: Andrew Harvey, 1989, (Cambridge University Press), 554 pp., ISBN 0-521-32196-4
- Dynamic Mixture Models for Multiple Time-Series
- Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Pattern Recognition and Machine Learning
- Improved Tracking of Multiple Humans with Trajectory Predcition and Occlusion Modeling
- Complex analysis for mathematics and engineering
- Lecture Notes in Statistics 101: Linear and Graphical Models for the Multivariate Complex Normal Distribution
- Latent Semantic Indexing (LSI) and TREC-2
- Less is More: Compact Matrix Decomposition for Large Sparse Graphs
- Towards Discovering Data Center Genome Using Sensor Nets
- Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services
- Efficient Retrieval of Similar Time Sequences Using DFT
- Parameter estimation for linear dynamical systems
- Introduction to statistical pattern recognition (2nd ed.)
- Fast feature selection using fractal dimension
- The coordination of arm movements: an experimentally confirmed mathematical model
- Weatherman: Automated, Online and Predictive Thermal Mapping and Management for Data Centers
Cited by
- Diagnosing Performance Changes by Comparing Request Flows
- A distance based time series classification framework
- Analysis of Recurrent Linear Networks for Enabling Compressed Sensing of Time-Varying Signals
- Relating observability and compressed sensing of time-varying signals in recurrent linear networks
- Understanding the Role of Dynamics in Brain Networks: Methods, Theory and Application
- NetDyna: Mining Networked Coevolving Time Series with Missing Values
- CoGenT: A Unified Contrastive-Generative Framework for Time Series Classification
- A Unified Contrastive-Generative Framework for Time Series Classification
- Mining of Sensor Data in Healthcare: A Survey
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