Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Fourier Feature Approximations for Periodic Kernels in Time-Series Modelling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| seed periodic hyperparameter with FFT instead of uniform random (ablation) | CVFF · Airline | RMSE: 135.26 -> 55.52 (P=1) | Helped |
| seed periodic hyperparameter with FFT instead of uniform random (ablation) | CVFF · Melbourne | RMSE: 6.21 -> 4.16 (P=1) | Helped |
| increase number of seeded periodicities P with uniform random init (ablation) | CVFF · Airline | RMSE: 135.26 -> 65.85 (P=1 to 3, non-monotonic: 225.4 at P=2) | No clear effect |
| use compositional variational Fourier features instead of variational RFF with RBF | VRFF · Airline | RMSE: 251.651 -> 16.984 | Helped |
| use CVFF instead of LSTM/GRU | RNN baselines · Melbourne | RMSE: 6.347 (GRU) -> 4.201 | Helped |
| use CVFF instead of full compositional GP | CFGP · CO2 | RMSE: 2.396 -> 2.433 | No clear effect |
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