Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
Authors: Felipe Tobar, Thang D. Bui, Richard E. Turner
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed GPCM is validated using synthetic and real-world signals. and 4 Experiments The DSE-GPCM was tested using synthetic data with known statistical properties and real-world signals. |
| Researcher Affiliation | Academia | Felipe Tobar ftobar@dim.uchile.cl Center for Mathematical Modeling Universidad de Chile Thang D. Bui tdb40@cam.ac.uk Department of Engineering University of Cambridge Richard E. Turner ret26@cam.ac.uk Department of Engineering University of Cambridge |
| Pseudocode | No | No pseudocode or algorithm blocks explicitly labeled as such were found. |
| Open Source Code | No | No explicit statement or link providing access to source code for the methodology described was found. |
| Open Datasets | Yes | We first analysed the Mauna Loa monthly CO2 concentration (de-trended). and The next experiment consisted of recovering the spectrum of an audio signal from the TIMIT corpus, composed of 1750 samples (at 16k Hz), only using an irregularly-sampled 20% of the available data. |
| Dataset Splits | No | The experiment then consisted of (i) learning the underlying kernel, (ii) estimating the latent process and (iii) performing imputation by removing observations in the region [-4.4, 4.4] (10% of the observations). and All methods used all the data |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) were mentioned. |
| Experiment Setup | Yes | We chose 88 inducing points for ux, that is, 1/10 of the samples to be recovered and 30 for uh; the hyperparameters in eq. (2) were set to γ = 0.45 and α = 0.1, so as to allow for an uninformative prior on h(t). The variational objective F was optimised with respect to the hyperparameter σh and the variational parameters µh, µx (means) and the Cholesky factors of Ch, Cx (covariances) using conjugate gradients. |