Flexible and efficient emulation of spatial extremes processes via variational autoencoders

• URL: http://arxiv.org/abs/2307.08079v4
• Date: Wed, 18 Dec 2024 14:46:23 GMT
• Title: Flexible and efficient emulation of spatial extremes processes via variational autoencoders
• Authors: Likun Zhang, Xiaoyu Ma, Christopher K. Wikle, Raphaël Huser,
• Abstract summary: We integrate a new spatial extremes model that has flexible and non-stationary dependence properties in the encoding-decoding structure of a variational autoencoder called the XVAE.<n> XVAE can emulate spatial observations and produce outputs that have the same statistical properties as the inputs, especially in the tail.<n>We analyze a high-resolution satellite-derived dataset of sea surface temperature in the Red Sea, which includes 30 years of daily measurements at 16703 grid cells.
• Score: 9.09823450442456
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions. In this paper, we aim to push the boundaries on computation and modeling of high-dimensional spatial extremes via integrating a new spatial extremes model that has flexible and non-stationary dependence properties in the encoding-decoding structure of a variational autoencoder called the XVAE. The XVAE can emulate spatial observations and produce outputs that have the same statistical properties as the inputs, especially in the tail. Our approach also provides a novel way of making fast inference with complex extreme-value processes. Through extensive simulation studies, we show that our XVAE is substantially more time-efficient than traditional Bayesian inference while outperforming many spatial extremes models with a stationary dependence structure. Lastly, we analyze a high-resolution satellite-derived dataset of sea surface temperature in the Red Sea, which includes 30 years of daily measurements at 16703 grid cells. We demonstrate how to use XVAE to identify regions susceptible to marine heatwaves under climate change and examine the spatial and temporal variability of the extremal dependence structure.

Related papers

• Interpretable Time Series Autoregression for Periodicity Quantification [18.6300875919604]
  Time series autoregression (AR) is a classical tool for auto-correlations and periodic structures in real-world systems.<n>We revisit this model by introducing sparse autoregression (SAR), where $ell$-norm constraints are used to isolate dominant periodicities.<n>We validate our framework on large-scale mobility and climate time series.
  arXiv  Detail & Related papers  (2025-06-28T14:17:11Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations [0.0]
  Inference for stationary and isotropic models is considerably easier, but the assumptions that underpin these models are rarely met by data observed over large or topographically complex domains.<n>A possible approach for accommodating nonstationarity in a spatial model is to warp the spatial domain to a latent space where stationarity and isotropy can be reasonably assumed.<n>We overcome these challenges by developing deep compositional spatial models to capture nonstationarity in extremal dependence.
  arXiv  Detail & Related papers  (2025-05-18T21:22:00Z)
• Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks [14.37149160708975]
  We present a spatial autoregressive modeling framework, which maps observations at a location and its neighbors to independent random variables.<n>In particular, we consider the SAR model with Generalized Extreme Value distribution innovations to combine the observation at a central grid location with its neighbors.<n>We apply this model to analyze annual maximum precipitation data from ERA-Interim-driven Weather Research and Forecasting (WRF) simulations.
  arXiv  Detail & Related papers  (2025-05-05T21:26:02Z)
• A Neural Operator-Based Emulator for Regional Shallow Water Dynamics [5.09419041446345]
  Coastal regions are particularly vulnerable to the impacts of rising sea levels and extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas is essential for infrastructure planning and climate adaptation. We present a novel autoregressive neural emulator that employs dimensionality reduction to efficiently approximate high-dimensional numerical solvers.
  arXiv  Detail & Related papers  (2025-02-20T18:02:44Z)
• A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling [0.0]
  We present a class of covariance functions that relies on fixed, observable spatial information. This model allows for separate structures for different sources of nonstationarity, such as marginal standard deviation, geometric anisotropy, and smoothness. We analyze the capabilities of the presented model through simulation studies and an application to Swiss precipitation data.
  arXiv  Detail & Related papers  (2024-10-22T05:53:25Z)
• Geometric Trajectory Diffusion Models [58.853975433383326]
  Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
  arXiv  Detail & Related papers  (2024-10-16T20:36:41Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective [63.60312929416228]
  textbftextitAttraos incorporates chaos theory into long-term time series forecasting. We show that Attraos outperforms various LTSF methods on mainstream datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
  arXiv  Detail & Related papers  (2024-02-18T05:35:01Z)
• Mitigation of Spatial Nonstationarity with Vision Transformers [1.690637178959708]
  We show the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance. We propose the mitigation of such impacts using self-attention (vision transformer) models.
  arXiv  Detail & Related papers  (2022-12-09T02:16:05Z)
• Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression [12.923271427789267]
  We develop a time-reduced-rank vector autoregression model whose coefficient are parameterized by low-rank tensor factorization. In the temporal context, the complex time-varying system behaviors can be revealed by the temporal modes in the proposed model.
  arXiv  Detail & Related papers  (2022-11-28T15:59:52Z)
• Learning to Accelerate Partial Differential Equations via Latent Global Evolution [64.72624347511498]
  Latent Evolution of PDEs (LE-PDE) is a simple, fast and scalable method to accelerate the simulation and inverse optimization of PDEs. We introduce new learning objectives to effectively learn such latent dynamics to ensure long-term stability. We demonstrate up to 128x reduction in the dimensions to update, and up to 15x improvement in speed, while achieving competitive accuracy.
  arXiv  Detail & Related papers  (2022-06-15T17:31:24Z)
• Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks [0.1469945565246172]
  In statistics, extreme value theory is often used to model spatial extremes. Here we combine GANs with extreme value theory (evtGAN) to model spatial dependencies in summer maxima of temperature and winter maxima in precipitation. Our results show that evtGAN outperforms classical GANs and standard statistical approaches to model spatial extremes.
  arXiv  Detail & Related papers  (2021-10-30T15:05:43Z)
• DeepClimGAN: A High-Resolution Climate Data Generator [60.59639064716545]
  Earth system models (ESMs) are often used to generate future projections of climate change scenarios. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
  arXiv  Detail & Related papers  (2020-11-23T20:13:37Z)
• Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting [16.934920617960085]
  DSARF approximates high dimensional data by a product variables between time dependent weights and spatially dependent factors. DSARF is different from the state-of-the-art techniques in that it parameterizes the weights in terms of a deep switching vector auto-regressive factorization. Our experiments attest the superior performance of DSARF in terms of long- and short-term prediction error, when compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-10T20:15:59Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.