Research map / Time series / Causal and generative time series
Non-Stationary Spectral Kernels
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use input-dependent frequency, lengthscale, and weight functions in the GSM kernel instead of stationary spectral components | Gaussian process regression · Simulated time series with a decreasing frequency component | frequency-pattern fit | Helped |
| Use GSM instead of stationary SM | Texture image prediction · Metal and wood textures | visual prediction and extrapolation quality | Helped |
| Use GSM instead of sparse spectrum (SS) | Texture image prediction · Metal and wood textures | visual prediction quality | Helped |
| Use GSM instead of the squared exponential (SE) kernel | Texture image prediction · Metal and wood textures | visual prediction and extrapolation quality | Helped |
| Use GSM instead of stationary SM | Texture image prediction · Wood texture missing cross-section | pattern reconstruction | Helped |
| Fit spatio-temporal land temperatures with GSM | Gaussian process regression · North American land surface temperatures | future-month prediction | Helped |
Closest papers by meaning
- Revisiting Nonstationary Kernel Design for Multi-Output Gaussian Processes (ICLR 2026 · similarity 0.91)
- Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase (ICML 2020 · similarity 0.89)
- Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning (NeurIPS 2020 · similarity 0.88)
- Nonstationary Sparse Spectral Permanental Process (NeurIPS 2024 · similarity 0.88)
- Stationarity without mean reversion in improper Gaussian processes (ICML 2024 · similarity 0.86)
- KernelMatmul: Scaling Gaussian Processes to Large Time Series (AAAI 2025 · similarity 0.86)
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