Time Series Analysis via Network Science: Concepts and Algorithms

• URL: http://arxiv.org/abs/2110.09887v1
• Date: Mon, 11 Oct 2021 13:33:18 GMT
• Title: Time Series Analysis via Network Science: Concepts and Algorithms
• Authors: Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro and Fernando Silva
• Abstract summary: This review provides a comprehensive overview of existing mapping methods for transforming time series into networks. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature and solid field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.

Related papers

• On the Regularization of Learnable Embeddings for Time Series Processing [18.069747511100132]
  We investigate methods to regularize the learning of local learnable embeddings for time series processing. We show that methods preventing the co-adaptation of local and global parameters are particularly effective in this context.
  arXiv  Detail & Related papers  (2024-10-18T17:30:20Z)
• Towards Generalisable Time Series Understanding Across Domains [10.350643783811174]
  We introduce OTiS, an open model for general time series analysis. We propose a novel pre-training paradigm including a tokeniser with learnable domain-specific signatures. Our model is pre-trained on a large corpus of 640,187 samples and 11 billion time points spanning 8 distinct domains.
  arXiv  Detail & Related papers  (2024-10-09T17:09:30Z)
• 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)
• Foundation Models for Time Series Analysis: A Tutorial and Survey [70.43311272903334]
  Foundation Models (FMs) have fundamentally reshaped the paradigm of model design for time series analysis. This survey aims to furnish a comprehensive and up-to-date overview of FMs for time series analysis.
  arXiv  Detail & Related papers  (2024-03-21T10:08:37Z)
• Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights [34.1973242428317]
  Time series data is ubiquitous across various domains such as finance, healthcare, and manufacturing. The ability to perform Content-based Time Series Retrieval (CTSR) is crucial for identifying unknown time series examples. We introduce a CTSR benchmark dataset that comprises time series data from a variety of domains.
  arXiv  Detail & Related papers  (2023-11-05T04:12:13Z)
• Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects [84.6945070729684]
  Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. This article reviews current state-of-the-art SSL methods for time series data.
  arXiv  Detail & Related papers  (2023-06-16T18:23:10Z)
• Novel Features for Time Series Analysis: A Complex Networks Approach [62.997667081978825]
  Time series data are ubiquitous in several domains as climate, economics and health care. Recent conceptual approach relies on time series mapping to complex networks. Network analysis can be used to characterize different types of time series.
  arXiv  Detail & Related papers  (2021-10-11T13:46:28Z)
• Deep learning for time series classification [2.0305676256390934]
  Time series analysis allows us to visualize and understand the evolution of a process over time. Time series classification consists of constructing algorithms dedicated to automatically label time series data. Deep learning has emerged as one of the most effective methods for tackling the supervised classification task.
  arXiv  Detail & Related papers  (2020-10-01T17:38:40Z)
• 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.