Vector Quantized Time Series Generation with a Bidirectional Prior Model

• URL: http://arxiv.org/abs/2303.04743v3
• Date: Sun, 2 Apr 2023 02:25:06 GMT
• Title: Vector Quantized Time Series Generation with a Bidirectional Prior Model
• Authors: Daesoo Lee, Sara Malacarne and Erlend Aune
• Abstract summary: Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants. We propose TimeVQVAE, the first work, to our knowledge, that uses vector quantization (VQ) techniques to address the TSG problem. We also propose VQ modeling in a time-frequency domain, separated into low-frequency (LF) and high-frequency (HF)
• Score: 0.3867363075280544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants. However, the fundamental limitations and challenges of training GANs still remain. In addition, the RNN-family typically has difficulties with temporal consistency between distant timesteps. Motivated by the successes in the image generation (IMG) domain, we propose TimeVQVAE, the first work, to our knowledge, that uses vector quantization (VQ) techniques to address the TSG problem. Moreover, the priors of the discrete latent spaces are learned with bidirectional transformer models that can better capture global temporal consistency. We also propose VQ modeling in a time-frequency domain, separated into low-frequency (LF) and high-frequency (HF). This allows us to retain important characteristics of the time series and, in turn, generate new synthetic signals that are of better quality, with sharper changes in modularity, than its competing TSG methods. Our experimental evaluation is conducted on all datasets from the UCR archive, using well-established metrics in the IMG literature, such as Fr\'echet inception distance and inception scores. Our implementation on GitHub: \url{https://github.com/ML4ITS/TimeVQVAE}.

Related papers

• Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis [31.43159668073136]
  Unsupervised anomaly detection in time series is essential in industrial applications, as it significantly reduces the need for manual intervention. Traditional methods use Graph Neural Networks (GNNs) or Transformers to analyze spatial while RNNs to model temporal dependencies. This paper introduces a novel temporal model built on an enhanced Graph Attention Network (GAT) for multivariate time series anomaly detection called TopoGDN.
  arXiv  Detail & Related papers  (2024-08-23T14:06:30Z)
• Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics [2.9578022754506605]
  In skeletal-based action recognition, Graph Convolutional Networks (GCNs) face limitations due to their complexity and high energy consumption. We propose a Signal-SGN(Spiking Graph Convolutional Network), which leverages the temporal dimension of skeletal sequences as the spiking timestep. Our experiments show that the proposed models not only surpass existing SNN-based methods in accuracy but also reduce computational storage costs during training.
  arXiv  Detail & Related papers  (2024-08-03T07:47:16Z)
• 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)
• Diagnostic Spatio-temporal Transformer with Faithful Encoding [54.02712048973161]
  This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex-temporal (ST) dependency. We formalize the problem as supervised dependency discovery, where the ST dependency is learned as a side product of time-series classification. We show that temporal positional encoding used in existing ST transformer works has a serious limitation capturing frequencies in higher frequencies (short time scales) We also propose a new ST dependency discovery framework, which can provide readily consumable diagnostic information in both spatial and temporal directions.
  arXiv  Detail & Related papers  (2023-05-26T05:31:23Z)
• It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting [1.278093617645299]
  We propose a framework for learning temporal and spatial dependencies using Graph Neural Network (GNN) networks. GNNs represent every sample as its own node in a graph, rather than all measurements for a particular location as a single node. The framework does not require measurements along the temporal dimension, meaning that it also facilitates irregular time series, different frequencies or missing data, without the need for data sampling imputation.
  arXiv  Detail & Related papers  (2023-03-23T11:16:33Z)
• FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification [53.55504611255664]
  FormerTime is a hierarchical representation model for improving the classification capacity for the multivariate time series classification task. It exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism.
  arXiv  Detail & Related papers  (2023-02-20T07:46:14Z)
• An Improved Time Feedforward Connections Recurrent Neural Networks [3.0965505512285967]
  Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. Traditional RNNs models amplify the gradient issue due to the strict time serial dependency. An improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A novel cell structure named Single Gate Recurrent Unit (SGRU) was presented to reduce the number of parameters for RNNs cell.
  arXiv  Detail & Related papers  (2022-11-03T09:32:39Z)
• Space-Time Graph Neural Networks with Stochastic Graph Perturbations [100.31591011966603]
  Space-time graph neural networks (ST-GNNs) learn efficient graph representations of time-varying data. In this paper we revisit the properties of ST-GNNs and prove that they are stable to graph stabilitys. Our analysis suggests that ST-GNNs are suitable for transfer learning on time-varying graphs.
  arXiv  Detail & Related papers  (2022-10-28T16:59:51Z)
• Towards Generating Real-World Time Series Data [52.51620668470388]
  We propose a novel generative framework for time series data generation - RTSGAN. RTSGAN learns an encoder-decoder module which provides a mapping between a time series instance and a fixed-dimension latent vector. To generate time series with missing values, we further equip RTSGAN with an observation embedding layer and a decide-and-generate decoder.
  arXiv  Detail & Related papers  (2021-11-16T11:31:37Z)
• Space-Time Graph Neural Networks [104.55175325870195]
  We introduce space-time graph neural network (ST-GNN) to jointly process the underlying space-time topology of time-varying network data. Our analysis shows that small variations in the network topology and time evolution of a system does not significantly affect the performance of ST-GNNs.
  arXiv  Detail & Related papers  (2021-10-06T16:08:44Z)
• Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging [3.4386226615580107]
  SATCN is a universal and flexible framework to performtemporal kriging for various datasets without need for model specification. We capture nodes by temporal convolutional networks, which allows our model to cope with data of diverse sizes. We conduct extensive experiments on three real-world datasets, including traffic and climate recordings.
  arXiv  Detail & Related papers  (2021-09-24T18:43:07Z)

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.