Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics

• URL: http://arxiv.org/abs/2203.08655v1
• Date: Wed, 16 Mar 2022 14:44:05 GMT
• Title: Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics
• Authors: Priyabrata Saha and Saibal Mukhopadhyay
• Abstract summary: We propose a model that learns to predict unknown dynamics using data from sparsely-distributed data sites. We demonstrate the advantage of our approach using both synthetic and real-world climate data.
• Score: 12.627823168264209
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations. Most of the existing deep learning models for modeling spatiotemporal dynamics are either designed for data in a regular grid or struggle to uncover the spatial relations from sparse and irregularly-spaced data sites. We propose a deep learning model that learns to predict unknown spatiotemporal dynamics using data from sparsely-distributed data sites. We base our approach on Radial Basis Function (RBF) collocation method which is often used for meshfree solution of partial differential equations (PDEs). The RBF framework allows us to unravel the observed spatiotemporal function and learn the spatial interactions among data sites on the RBF-space. The learned spatial features are then used to compose multilevel transformations of the raw observations and predict its evolution in future time steps. We demonstrate the advantage of our approach using both synthetic and real-world climate data.

Related papers

• FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction [29.117905766777735]
  Mobile devices and the Internet of Things (IoT) devices nowadays generate a large amount of heterogeneous spatial-temporal data. It remains a challenging problem to model the spatial-temporal dynamics under privacy concern. We propose a novel Federated Adaptive spatial-temporal Attention (FedASTA) framework to model the dynamic spatial-temporal relations.
  arXiv  Detail & Related papers  (2024-05-21T11:44:07Z)
• Spatio-Temporal Branching for Motion Prediction using Motion Increments [55.68088298632865]
  Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications. Traditional methods rely on hand-crafted features and machine learning techniques. We propose a noveltemporal-temporal branching network using incremental information for HMP.
  arXiv  Detail & Related papers  (2023-08-02T12:04:28Z)
• Reconstructing Spatiotemporal Data with C-VAEs [49.1574468325115]
  Conditional continuous representation of moving regions is commonly used. In this work, we explore the capabilities of Conditional Varitemporal Autoencoder (C-VAE) models to generate realistic representations of regions' evolution.
  arXiv  Detail & Related papers  (2023-07-12T15:34:10Z)
• OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning [67.07363529640784]
  We propose OpenSTL to categorize prevalent approaches into recurrent-based and recurrent-free models. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and forecasting weather. We find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models.
  arXiv  Detail & Related papers  (2023-06-20T03:02:14Z)
• Multi-Temporal Relationship Inference in Urban Areas [75.86026742632528]
  Finding temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. We propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity.
  arXiv  Detail & Related papers  (2023-06-15T07:48:32Z)
• VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
  Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables. The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
  arXiv  Detail & Related papers  (2023-04-03T13:13:19Z)
• Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility [5.864710987890994]
  Deep GA-vLS assumes data to be of fixed and regular shaped tensor shaped and face challenges of handling irregular data. We present a novel geo-aware enabled learning operation based on a novel data structure for dependencies while maintaining the recurrent mechanism.
  arXiv  Detail & Related papers  (2022-11-27T16:51:28Z)
• Continuous PDE Dynamics Forecasting with Implicit Neural Representations [24.460010868042758]
  We introduce a new data-driven, approach to PDEs flow with continuous-time dynamics of spatially continuous functions. This is achieved by embedding spatial extrapolation independently of their discretization via Implicit Neural Representations. It extrapolates at arbitrary spatial and temporal locations; it can learn sparse grids or irregular data at test time, it generalizes to new grids or resolutions.
  arXiv  Detail & Related papers  (2022-09-29T15:17:50Z)
• STONet: A Neural-Operator-Driven Spatio-temporal Network [38.5696882090282]
  Graph-based graph-temporal neural networks are effective to model spatial dependency among discrete points sampled irregularly. We propose atemporal framework based on neural operators for PDEs, which learn the mechanisms governing the dynamics of spatially-continuous physical quantities. Experiments show our model's performance on forecasting spatially-continuous physic quantities, and its superior to unseen spatial points and ability to handle temporally-irregular data.
  arXiv  Detail & Related papers  (2022-04-18T17:20:12Z)
• CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations [72.4716073597902]
  We propose a method to learn object Canonical Point Cloud Representations of dynamically or moving objects. We demonstrate the effectiveness of our method on several applications including shape reconstruction, camera pose estimation, continuoustemporal sequence reconstruction, and correspondence estimation.
  arXiv  Detail & Related papers  (2020-08-06T17:58:48Z)
• A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning [0.0]
  We introduce here a framework for decomposed-temporal prediction of climate and environmental data using deep learning. Specifically, we introduce functions which can be spatially and mapped on a regular grid allowing the reconstruction of complete-temporal-signal. Applications on simulated real-world data will show the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2020-07-23T07:44:04Z)

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.