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Symbolic analysis-based reduced order Markov modeling of time series data
Devesh K. Jha
, Nurali Virani
,
Jan Reimann
, Abhishek Srivastav
,
Asok Ray
Mathematics
Mechanical Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
17
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Dive into the research topics of 'Symbolic analysis-based reduced order Markov modeling of time series data'. Together they form a unique fingerprint.
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Keyphrases
Time Series Data
100%
Markov Modeling
100%
Markov Model
100%
Order Reduction
100%
Reduced Order Model
100%
Symbolic Analysis
100%
Symbol Sequence
75%
Bayesian Inference
50%
Original Model
50%
Inference Rules
50%
Reduced-scale Model
50%
Operating Conditions
25%
Special Class
25%
Probabilistic Automata
25%
Spectral Properties
25%
Data Representation
25%
Information Theory
25%
Symbolic Dynamics
25%
State Space
25%
Model Size
25%
Time Series Signal
25%
Non-deterministic
25%
Algebraic Structure
25%
Pressure Oscillation
25%
Swirl Combustor
25%
Hamming Distance
25%
Agglomerative Hierarchical Clustering
25%
Reduced Order Modeling
25%
Combustion Regime
25%
Data Learning
25%
Rolling Element Bearing
25%
Temporal Memory
25%
First-order Markov Model
25%
Information Theoretic Criteria
25%
Stochastic Matrix
25%
Public Dataset
25%
Condition Change
25%
Flame Instability
25%
Small States
25%
Mathematics
Symbol Sequence
100%
Time Series Data
100%
Bayesian Inference
66%
Time Series
66%
Time Series Analysis
66%
Hierarchical Clustering
33%
Algebraic Structure
33%
Symbolic Dynamic
33%
Spectral Property
33%
Hamming Distance
33%
Stochastic Matrix
33%
Combustor
33%
Computer Science
Time Series Data
100%
Inference Rule
100%
State Automaton
50%
Data Representation
50%
State Space
50%
Operating Condition
50%
Hamming Distance
50%
Public Data Set
50%
Spectral Property
50%
Hierarchical Clustering
50%