Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey

• URL: http://arxiv.org/abs/2409.09106v1
• Date: Fri, 13 Sep 2024 14:19:44 GMT
• Title: Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey
• Authors: Mansura Habiba, Barak A. Pearlmutter, Mehrdad Maleki,
• Abstract summary: 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.
• Score: 0.18434042562191813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continuous-time series is essential for different modern application areas, e.g. healthcare, automobile, energy, finance, Internet of things (IoT) and other related areas. Different application needs to process as well as analyse a massive amount of data in time series structure in order to determine the data-driven result, for example, financial trend prediction, potential probability of the occurrence of a particular event occurrence identification, patient health record processing and so many more. However, modeling real-time data using a continuous-time series is challenging since the dynamical systems behind the data could be a differential equation. Several research works have tried to solve the challenges of modelling the continuous-time series using different neural network models and approaches for data processing and learning. The existing deep learning models are not free from challenges and limitations due to diversity among different attributes, behaviour, duration of steps, energy, and data sampling rate. This paper has described the general problem domain of time series and reviewed the challenges of modelling the continuous time series. We have presented a comparative analysis of recent developments in deep learning models and their contribution to solving different difficulties of modelling the continuous time series. We have also identified the limitations of the existing neural network model and open issues. The main goal of this review is to understand the recent trend of neural network models used in a different real-world application with continuous-time data.

Related papers

• Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
  Time series data is of great significance in real-world scenarios. Recent years have witnessed remarkable breakthroughs in the time series community. We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• 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)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations [15.797295258800638]
  We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data. Our method relies on a continuous-time-dependent model of the series' evolution dynamics. A modulation mechanism, driven by a meta-learning algorithm, allows adaptation to unseen samples and extrapolation beyond observed time-windows.
  arXiv  Detail & Related papers  (2023-06-09T13:20:04Z)
• 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)
• 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)
• 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)
• CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data [1.8352113484137622]
  In this paper, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive model that is trainable end-to-end using neural networks modulated by time lags. It is applied to multivariate time-series regression tasks using data provided for Alzheimer's disease progression modeling and intensive care unit (ICU) mortality rate prediction.
  arXiv  Detail & Related papers  (2021-04-08T12:43:44Z)
• 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)
• A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series [18.224344440110862]
  Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, and health. We first describe several axes along which approaches to learning from irregularly sampled time series differ. We then survey the recent literature organized primarily along the axis of modeling primitives.
  arXiv  Detail & Related papers  (2020-11-30T23:41:47Z)

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.