Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification

• URL: http://arxiv.org/abs/2410.09307v1
• Date: Sat, 12 Oct 2024 00:03:40 GMT
• Title: Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification
• Authors: Paulo Coelho, Raul Araju, Luís Ramos, Samir Saliba, Renato Vimieiro,
• Abstract summary: This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification. By representing time series as visibility graphs, it is possible to encode both temporal dependencies inherent to time series data. Our architecture is fully modular, enabling flexible experimentation with different models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification, based on visibility graph representations. Traditional time series classification methods often struggle with high computational complexity and inadequate capture of spatio-temporal dynamics. By representing time series as visibility graphs, it is possible to encode both spatial and temporal dependencies inherent to time series data, while being computationally efficient. Our architecture is fully modular, enabling flexible experimentation with different models and representations. We employ directed visibility graphs encoded with in-degree and PageRank features to improve the representation of time series, ensuring efficient computation while enhancing the model's ability to capture long-range dependencies in the data. We show the robustness and generalization capability of the proposed architecture across a diverse set of classification tasks and against a traditional model. Our work represents a significant advancement in the application of GNNs for time series analysis, offering a powerful and flexible framework for future research and practical implementations.

Related papers

• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting [20.03223916749058]
  Time series forecasting lies at the core of important real-world applications in science and engineering. We propose TimeGNN, a method that learns dynamic temporal graph representations. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods.
  arXiv  Detail & Related papers  (2023-07-27T08:10:19Z)
• 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)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification [6.76723360505692]
  We propose a novel temporal dynamic neural graph network (TodyNet) that can extract hidden-temporal dependencies without undefined graph structure. The experiments on 26 UEA benchmark datasets illustrate that the proposed TodyNet outperforms existing deep learning-based methods in the MTSC tasks.
  arXiv  Detail & Related papers  (2023-04-11T09:21:28Z)
• Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting [50.901984244738806]
  We show how to model the evolutionary and multi-scale interactions of time series. In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations. A unified neural network is provided to integrate the components above to get the final prediction.
  arXiv  Detail & Related papers  (2022-06-28T08:11:12Z)
• Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations [11.486068333583216]
  This paper tackles the problem of learning effective models to reconstruct missing data points. We propose a class of attention-based architectures, that given a set of highly sparse observations, learn a representation for points in time and space. Compared to the state of the art, our model handles sparse data without propagating prediction errors or requiring a bidirectional model to encode forward and backward time dependencies.
  arXiv  Detail & Related papers  (2022-05-26T16:40:48Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification [16.64345034889185]
  We propose a hybrid neural architecture, called Self-Attentive Recurrent Convolutional Networks (SARCoN) SARCoN is the synthesis of long short-term memory networks with self-attentive mechanisms and Fully Convolutional Networks. Our work provides a novel angle that deepens the understanding of time series classification, qualifying our proposed model as an ideal choice for real-world applications.
  arXiv  Detail & Related papers  (2020-12-21T16:42:07Z)
• From Static to Dynamic Node Embeddings [61.58641072424504]
  We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropriate graph time-series representation. We find that the top-3 temporal models are always those that leverage the new $epsilon$-graph time-series representation.
  arXiv  Detail & Related papers  (2020-09-21T16:48:29Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

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.