Reduced Order Modeling with Skew-Radial Basis Functions for Time Series Prediction †
Abstract
:1. Introduction
2. Sparse Skew RBFs
3. Numerical Results
3.1. Details of Implementation
3.2. Mackey–Glass Revisited
3.3. Mouse Telemetry Data
3.4. Other Applications
3.4.1. Iterated Prediction
3.4.2. Visualization of Trained sRBF Models
3.4.3. Anomaly Detection
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Number of Centers = 50 | ||||
---|---|---|---|---|
Sparsity | Train Acc | Val Acc | RBFs | |
0 | 26.84 | 0.0013 | 0.0012 | 50 |
0.0001 | 19.47 | 0.0009 | 0.0007 | 50 |
0.002 | 2.14 | 0.0024 | 0.0021 | 2 |
0.01 | 1.66 | 0.0036 | 0.0033 | 1 |
0.1 | 1.28 | 0.0177 | 0.0169 | 1 |
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Aminian, M.; Kirby, M. Reduced Order Modeling with Skew-Radial Basis Functions for Time Series Prediction. Eng. Proc. 2023, 39, 93. https://doi.org/10.3390/engproc2023039093
Aminian M, Kirby M. Reduced Order Modeling with Skew-Radial Basis Functions for Time Series Prediction. Engineering Proceedings. 2023; 39(1):93. https://doi.org/10.3390/engproc2023039093
Chicago/Turabian StyleAminian, Manuchehr, and Michael Kirby. 2023. "Reduced Order Modeling with Skew-Radial Basis Functions for Time Series Prediction" Engineering Proceedings 39, no. 1: 93. https://doi.org/10.3390/engproc2023039093