Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 24th IEEE International Conference on Data Mining, ICDM 2024 |
| Editors | Elena Baralis, Kun Zhang, Ernesto Damiani, Merouane Debbah, Panos Kalnis, Xindong Wu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 570-579 |
| Number of pages | 10 |
| ISBN (electronic) | 979-8-3315-0668-1 |
| ISBN (print) | 979-8-3315-0669-8 |
| Publication status | Published - 9 Dec 2024 |
| Event | 24th IEEE International Conference on Data Mining, ICDM 2024 - Abu Dhabi, United Arab Emirates Duration: 9 Dec 2024 → 12 Dec 2024 |
Publication series
| Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
|---|---|
| ISSN (Print) | 1550-4786 |
Abstract
Time Series Classification (TSC) aims to develop predictive models for discrete target variables using ordered, real-valued attributes. However, existing deep learning approaches face challenges in addressing the inherent dependencies across data dimensions and the dynamic characteristics of time series data, often resulting in insufficient feature extraction and suboptimal classification accuracy. This paper introduces HyperTime, a novel framework designed to overcome these challenges by modeling time series data as hypergraphs, where hyperedges connect arbitrary sets of nodes. HyperTime distinguishes itself by processing time series data into segmented windows, which form nodes in a hypergraph connected by hyperedges. This approach captures complex temporal relationships and dynamics within the data. The framework employs 'hypernode embedding', applying an attention mechanism to each time series dimension within a window, and 'hyperedge embedding', incorporating an LSTM layer for deeper temporal analysis. A key innovation in HyperTime is the 'hyper convolution' operation, which includes both hypernode and hyperedge convolutions. Nodes are convoluted using the embeddings of their associated hyperedges, enriching the learning and understanding of their role within the hypergraph. The hyperedge convolution layer integrates local and global information, aggregating node features to capture complex interactions and relationships. The effectiveness of HyperTime is validated through extensive experiments on 26 datasets from the UEA archive, the Human Activity Recognition (HAR) and PAMAP2 Physical Activity Monitoring (PAM) datasets, and 10 datasets from the UCR archive.
Keywords
- Dynamic hypergraph, Graph neural networks, Time series classification
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
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Proceedings - 24th IEEE International Conference on Data Mining, ICDM 2024. ed. / Elena Baralis; Kun Zhang; Ernesto Damiani; Merouane Debbah; Panos Kalnis; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2024. p. 570-579 (Proceedings - IEEE International Conference on Data Mining, ICDM).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - HyperTime
T2 - 24th IEEE International Conference on Data Mining, ICDM 2024
AU - Younis, Raneen
AU - Ahmadi, Zahra
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/12/9
Y1 - 2024/12/9
N2 - Time Series Classification (TSC) aims to develop predictive models for discrete target variables using ordered, real-valued attributes. However, existing deep learning approaches face challenges in addressing the inherent dependencies across data dimensions and the dynamic characteristics of time series data, often resulting in insufficient feature extraction and suboptimal classification accuracy. This paper introduces HyperTime, a novel framework designed to overcome these challenges by modeling time series data as hypergraphs, where hyperedges connect arbitrary sets of nodes. HyperTime distinguishes itself by processing time series data into segmented windows, which form nodes in a hypergraph connected by hyperedges. This approach captures complex temporal relationships and dynamics within the data. The framework employs 'hypernode embedding', applying an attention mechanism to each time series dimension within a window, and 'hyperedge embedding', incorporating an LSTM layer for deeper temporal analysis. A key innovation in HyperTime is the 'hyper convolution' operation, which includes both hypernode and hyperedge convolutions. Nodes are convoluted using the embeddings of their associated hyperedges, enriching the learning and understanding of their role within the hypergraph. The hyperedge convolution layer integrates local and global information, aggregating node features to capture complex interactions and relationships. The effectiveness of HyperTime is validated through extensive experiments on 26 datasets from the UEA archive, the Human Activity Recognition (HAR) and PAMAP2 Physical Activity Monitoring (PAM) datasets, and 10 datasets from the UCR archive.
AB - Time Series Classification (TSC) aims to develop predictive models for discrete target variables using ordered, real-valued attributes. However, existing deep learning approaches face challenges in addressing the inherent dependencies across data dimensions and the dynamic characteristics of time series data, often resulting in insufficient feature extraction and suboptimal classification accuracy. This paper introduces HyperTime, a novel framework designed to overcome these challenges by modeling time series data as hypergraphs, where hyperedges connect arbitrary sets of nodes. HyperTime distinguishes itself by processing time series data into segmented windows, which form nodes in a hypergraph connected by hyperedges. This approach captures complex temporal relationships and dynamics within the data. The framework employs 'hypernode embedding', applying an attention mechanism to each time series dimension within a window, and 'hyperedge embedding', incorporating an LSTM layer for deeper temporal analysis. A key innovation in HyperTime is the 'hyper convolution' operation, which includes both hypernode and hyperedge convolutions. Nodes are convoluted using the embeddings of their associated hyperedges, enriching the learning and understanding of their role within the hypergraph. The hyperedge convolution layer integrates local and global information, aggregating node features to capture complex interactions and relationships. The effectiveness of HyperTime is validated through extensive experiments on 26 datasets from the UEA archive, the Human Activity Recognition (HAR) and PAMAP2 Physical Activity Monitoring (PAM) datasets, and 10 datasets from the UCR archive.
KW - Dynamic hypergraph
KW - Graph neural networks
KW - Time series classification
U2 - 10.1109/ICDM59182.2024.00064
DO - 10.1109/ICDM59182.2024.00064
M3 - Conference contribution
AN - SCOPUS:86000213243
SN - 979-8-3315-0669-8
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 570
EP - 579
BT - Proceedings - 24th IEEE International Conference on Data Mining, ICDM 2024
A2 - Baralis, Elena
A2 - Zhang, Kun
A2 - Damiani, Ernesto
A2 - Debbah, Merouane
A2 - Kalnis, Panos
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2024 through 12 December 2024
ER -