DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series

• URL: http://arxiv.org/abs/2203.15009v2
• Date: Tue, 31 Oct 2023 16:50:03 GMT
• Title: DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
• Authors: Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert
• Abstract summary: Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics. Here we introduce DAMNETS, a scalable deep generative model for network time series.
• Score: 6.834250594353335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study. Designing flexible and scalable generative models is a very challenging task due to the high dimensionality of the data, as well as the need to represent temporal dependencies and marginal network structure. Here we introduce DAMNETS, a scalable deep generative model for network time series. DAMNETS outperforms competing methods on all of our measures of sample quality, over both real and synthetic data sets.

Related papers

• Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey [0.18434042562191813]
  Continuous-time series is essential for different modern application areas, e.g. healthcare, automobile, energy, finance, Internet of things (IoT) This paper has described the general problem domain of time series and reviewed the challenges of modelling the continuous time series.
  arXiv  Detail & Related papers  (2024-09-13T14:19:44Z)
• Generative Modeling for Tabular Data via Penalized Optimal Transport Network [2.0319002824093015]
  Wasserstein generative adversarial network (WGAN) is a notable improvement in generative modeling. We propose POTNet, a generative deep neural network based on a novel, robust, and interpretable marginally-penalized Wasserstein (MPW) loss.
  arXiv  Detail & Related papers  (2024-02-16T05:27:05Z)
• Latent variable model for high-dimensional point process with structured missingness [4.451479907610764]
  Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology. Real-world datasets can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown process. We propose a flexible and efficient latent-variable model that is capable of addressing all these limitations.
  arXiv  Detail & Related papers  (2024-02-08T15:41:48Z)
• MADS: Modulated Auto-Decoding SIREN for time series imputation [9.673093148930874]
  We propose MADS, a novel auto-decoding framework for time series imputation, built upon implicit neural representations. We evaluate our model on two real-world datasets, and show that it outperforms state-of-the-art methods for time series imputation.
  arXiv  Detail & Related papers  (2023-07-03T09:08:47Z)
• MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs [1.1756822700775666]
  We introduce a new framework for interpreting time series data by extracting and clustering the input representative patterns. We run experiments on eight datasets of the UCR/UEA archive, along with HAR and PAM datasets.
  arXiv  Detail & Related papers  (2023-06-06T16:24:27Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference [55.35176938713946]
  We develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its multi-stochastic-layer generative network. We propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a downward generative model. The efficacy and scalability of our models are demonstrated on both unsupervised and supervised learning tasks on big corpora.
  arXiv  Detail & Related papers  (2020-06-15T22:22:56Z)

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.