PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series

• URL: http://arxiv.org/abs/2110.00071v1
• Date: Thu, 30 Sep 2021 20:01:19 GMT
• Title: PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series
• Authors: Futoon M. Abushaqra, Hao Xue, Yongli Ren and Flora D. Salim
• Abstract summary: Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. We show that PIETS is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
• Score: 5.911865723926626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model; (2) parallelised neural networks to enable flexibility and avoid information overwhelming; and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.

Related papers

• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data [92.1255811066468]
  We review the use of diffusion models in time series and S-temporal data, categorizing them by model, task type, data modality, and practical application domain. We categorize diffusion models into unconditioned and conditioned types discuss time series and S-temporal data separately. Our survey covers their application extensively in various fields including healthcare, recommendation, climate, energy, audio, and transportation.
  arXiv  Detail & Related papers  (2024-04-29T17:19:40Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Stacking VAE with Graph Neural Networks for Effective and Interpretable Time Series Anomaly Detection [5.935707085640394]
  We propose a stacking variational auto-encoder (VAE) model with graph neural networks for the effective and interpretable time-series anomaly detection. We show that our proposed model outperforms the strong baselines on three public datasets with considerable improvements.
  arXiv  Detail & Related papers  (2021-05-18T09:50:00Z)
• Deep Time Series Models for Scarce Data [8.673181404172963]
  Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. Data scarcity is a universal issue that occurs in a vast range of data analytics problems.
  arXiv  Detail & Related papers  (2021-03-16T22:16:54Z)
• On Disentanglement in Gaussian Process Variational Autoencoders [3.403279506246879]
  We introduce a class of models recently introduced that have been successful in different tasks on time series data. Our model exploits the temporal structure of the data by modeling each latent channel with a GP prior and employing a structured variational distribution. We provide evidence that we can learn meaningful disentangled representations on real-world medical time series data.
  arXiv  Detail & Related papers  (2021-02-10T15:49:27Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Multivariate Time-series Anomaly Detection via Graph Attention Network [27.12694738711663]
  Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. One major limitation is that they do not capture the relationships between different time-series explicitly. We propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue.
  arXiv  Detail & Related papers  (2020-09-04T07:46:19Z)
• 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)
• Interpretable Deep Representation Learning from Temporal Multi-view Data [4.2179426073904995]
  We propose a generative model based on variational autoencoder and a recurrent neural network to infer the latent dynamics for multi-view temporal data. We invoke our proposed model for analyzing three datasets on which we demonstrate the effectiveness and the interpretability of the model.
  arXiv  Detail & Related papers  (2020-05-11T15:59:06Z)

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.