Novel Features for Time Series Analysis: A Complex Networks Approach
- URL: http://arxiv.org/abs/2110.09888v1
- Date: Mon, 11 Oct 2021 13:46:28 GMT
- Title: Novel Features for Time Series Analysis: A Complex Networks Approach
- Authors: Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro and Fernando
Silva
- Abstract summary: 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.
- Score: 62.997667081978825
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Time series data are ubiquitous in several domains as climate, economics and
health care. Mining features from these time series is a crucial task with a
multidisciplinary impact. Usually, these features are obtained from structural
characteristics of time series, such as trend, seasonality and autocorrelation,
sometimes requiring data transformations and parametric models. A recent
conceptual approach relies on time series mapping to complex networks, where
the network science methodologies can help characterize time series. In this
paper, we consider two mapping concepts, visibility and transition probability
and propose network topological measures as a new set of time series features.
To evaluate the usefulness of the proposed features, we address the problem of
time series clustering. More specifically, we propose a clustering method that
consists in mapping the time series into visibility graphs and quantile graphs,
calculating global topological metrics of the resulting networks, and using
data mining techniques to form clusters. We apply this method to a data sets of
synthetic and empirical time series. The results indicate that network-based
features capture the information encoded in each of the time series models,
resulting in high accuracy in a clustering task. Our results are promising and
show that network analysis can be used to characterize different types of time
series and that different mapping methods capture different characteristics of
the time series.
Related papers
- 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) - HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data.
In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed.
We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
arXiv Detail & Related papers (2022-08-11T14:05:51Z) - 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) - Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
We propose a three-stage framework for forecasting high-dimensional time-series data.
Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step.
When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
arXiv Detail & Related papers (2021-10-26T20:41:19Z) - Time Series Analysis via Network Science: Concepts and Algorithms [62.997667081978825]
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.
arXiv Detail & Related papers (2021-10-11T13:33:18Z) - Time Series is a Special Sequence: Forecasting with Sample Convolution
and Interaction [9.449017120452675]
Time series is a special type of sequence data, a set of observations collected at even intervals of time and ordered chronologically.
Existing deep learning techniques use generic sequence models for time series analysis, which ignore some of its unique properties.
We propose a novel neural network architecture and apply it for the time series forecasting problem, wherein we conduct sample convolution and interaction at multiple resolutions for temporal modeling.
arXiv Detail & Related papers (2021-06-17T08:15:04Z) - Multi-Time Attention Networks for Irregularly Sampled Time Series [18.224344440110862]
Irregular sampling occurs in many time series modeling applications.
We propose a new deep learning framework for this setting that we call Multi-Time Attention Networks.
Our results show that our approach performs as well or better than a range of baseline and recently proposed models.
arXiv Detail & Related papers (2021-01-25T18:57:42Z) - 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.