Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)

• URL: http://arxiv.org/abs/2208.14592v1
• Date: Wed, 31 Aug 2022 01:52:00 GMT
• Title: Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)
• Authors: Im\`ene R. Goumiri, Alec M. Dunton, Amanda L. Muyskens, Benjamin W. Priest, Robert E. Armstrong
• Abstract summary: Ground-based observations from commercial off the shelf (COTS) cameras remain inexpensive compared to higher precision instruments. limited sensor availability combined with noisier observations can produce gappy time-series data. Deep Neural Networks (DNNs) have become the tool of choice due to their empirical success at learning complex nonlinear embeddings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal variations of apparent magnitude, called light curves, are observational statistics of interest captured by telescopes over long periods of time. Light curves afford the exploration of Space Domain Awareness (SDA) objectives such as object identification or pose estimation as latent variable inference problems. Ground-based observations from commercial off the shelf (COTS) cameras remain inexpensive compared to higher precision instruments, however, limited sensor availability combined with noisier observations can produce gappy time-series data that can be difficult to model. These external factors confound the automated exploitation of light curves, which makes light curve prediction and extrapolation a crucial problem for applications. Traditionally, image or time-series completion problems have been approached with diffusion-based or exemplar-based methods. More recently, Deep Neural Networks (DNNs) have become the tool of choice due to their empirical success at learning complex nonlinear embeddings. However, DNNs often require large training data that are not necessarily available when looking at unique features of a light curve of a single satellite. In this paper, we present a novel approach to predicting missing and future data points of light curves using Gaussian Processes (GPs). GPs are non-linear probabilistic models that infer posterior distributions over functions and naturally quantify uncertainty. However, the cubic scaling of GP inference and training is a major barrier to their adoption in applications. In particular, a single light curve can feature hundreds of thousands of observations, which is well beyond the practical realization limits of a conventional GP on a single machine. Consequently, we employ MuyGPs, a scalable framework for hyperparameter estimation of GP models that uses nearest neighbors sparsification and local cross-validation. MuyGPs...

Related papers

• Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data [0.0]
  We introduce fully Bayesian Neural Networks (FBNNs) for active learning tasks in the'small data' regime. FBNNs provide reliable predictive distributions, crucial for making informed decisions under uncertainty in the active learning setting. Here, we assess the suitability and performance of FBNNs with the No-U-Turn Sampler for active learning tasks in the'small data' regime.
  arXiv  Detail & Related papers  (2024-05-16T05:20:47Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation [80.227864832092]
  Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. The sheer size of large-scale graphs presents a significant challenge to real-time inference with GNNs. We propose an online propagation framework and two novel node-adaptive propagation methods.
  arXiv  Detail & Related papers  (2023-10-17T05:03:00Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains [7.936841911281107]
  We propose cvHM, a general inference framework for latent GP models leveraging Hida-Mat'ern kernels and conjugate variational inference (CVI) We are able to perform variational inference of latent neural trajectories with linear time complexity for arbitrary likelihoods.
  arXiv  Detail & Related papers  (2023-06-01T16:31:36Z)
• FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels [82.53569355337586]
  This work offers an efficient solution to temporal point processes inference using general parametric kernels with finite support. The method's effectiveness is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG) Results show that the proposed approach leads to an improved estimation of pattern latency than the state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-10T12:35:02Z)
• Understanding of the properties of neural network approaches for transient light curve approximations [37.91290708320157]
  This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients.
  arXiv  Detail & Related papers  (2022-09-15T18:00:08Z)
• Supernova Light Curves Approximation based on Neural Network Models [53.180678723280145]
  Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. We study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve.
  arXiv  Detail & Related papers  (2022-06-27T13:46:51Z)
• Real-time detection of anomalies in large-scale transient surveys [0.0]
  We present two novel methods of automatically detecting anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies.
  arXiv  Detail & Related papers  (2021-10-29T18:29:25Z)
• Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes [48.64129867897491]
  We show that there is a simple and elegant way to combine pseudo-point methods with the state space GP approximation framework to get the best of both worlds. We demonstrate that the combined approach is more scalable and applicable to a greater range of epidemiology--temporal problems than either method on its own.
  arXiv  Detail & Related papers  (2021-06-18T16:30:09Z)
• Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models [12.229461458053809]
  We showcase how the derivative of a GP model can be used to provide an analytical error propagation formulation. We analyze the predictive variance and the propagated error terms in a temperature prediction problem from infrared sounding data.
  arXiv  Detail & Related papers  (2020-12-09T10:03:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.