Article
Computer Science, Information Systems
Agnieszka Jastrzebska
Summary: This paper proposes a new approach to time series classification by transforming scalar time series into a two-dimensional space of amplitude and change of amplitude and using visual pattern recognition for classification. The effectiveness of the method is demonstrated through experiments and comparison with state-of-the-art approaches. The conversion of raw time series into images and feature extraction opens up possibilities for applying standard clustering algorithms.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Fanling Huang, Yangdong Deng
Summary: Recent research has shown that supervised Convolutional Neural Networks (CNNs) are superior in learning hierarchical representations from time series data for successful classification. However, obtaining high-quality labeled time series data can be costly and infeasible. This study introduces a Time-series Convolutional GAN (TCGAN), which uses a generator and a discriminator to play an adversarial game and learns representations for time series recognition without label information. Experimental results demonstrate that TCGAN is faster and more accurate than existing time-series GANs, and the learned representations enable simple classification and clustering methods to achieve superior performance in scenarios with few-labeled and imbalanced-labeled data.
Article
Engineering, Civil
Masoud Haghani Chegeni, Mohammad Kazem Sharbatdar, Reza Mahjoub, Mahdi Raftari
Summary: The motivation of this article is to propose new damage classifiers based on supervised learning for damage localization and quantification. A new feature extraction approach using time series analysis is introduced to improve current feature extraction techniques in time series modeling. The proposed classifiers, based on the extracted features from the proposed approach, are able to locate and quantify damage, with the residual-based classifiers yielding better results than the coefficient-based classifiers. These methods are also superior to some classical techniques.
EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION
(2022)
Article
Computer Science, Artificial Intelligence
Jianping Gou, Jun Song, Lan Du, Shaoning Zeng, Yongzhao Zhan, Zhang Yi
Summary: This research introduces a novel method based on collaborative representation classification for image classification tasks, demonstrating superior performance and effective optimization. By incorporating innovative class mean-weighted and decorrelating regularization terms, it effectively enhances classification performance.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Mathematics
Achilleas Anastasiou, Peter Hatzopoulos, Alex Karagrigoriou, George Mavridoglou
Summary: This work focuses on developing new distance measure algorithms for analyzing causal relationships in financial and economic data. The proposed methodology was applied to a case study involving the classification of 19 EU countries based on health resource variables.
Article
Computer Science, Artificial Intelligence
Stefano Mauceri, James Sweeney, Miguel Nicolau, James McDermott
Summary: We propose a method to embed time series into a latent space where pairwise Euclidean distances equal pairwise dissimilarities in the original space. By using auto-encoder and encoder-only neural networks to learn elastic dissimilarity measures like dynamic time warping, we achieve classification performance close to that of raw data but with significantly lower dimensionality. This provides substantial savings in computational and storage requirements for nearest neighbor time series classification.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jianping Gou, Xiangshuo Xiong, Hongwei Wu, Lan Du, Shaoning Zeng, Yunhao Yuan, Weihua Ou
Summary: The representation and classification of testing samples are crucial in pattern recognition. Collaborative representation-based classification (CRC) is a promising approach that utilizes training samples to collaboratively represent and classify testing samples. However, most CRC methods fail to fully exploit the local and discrimination information. To overcome this limitation, a novel supervised CRC method called LWCCRC is proposed, which incorporates local constraints and competition to improve representation. Extensive experiments on different datasets demonstrate that LWCCRC outperforms state-of-the-art CRC methods significantly.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Y. M. Ospina-Davila, Mauricio Orozco-Alzate
Summary: This paper presents a new data representation method using dissimilarity pattern recognition and proximity learning, which provides highly discriminant dissimilarity-based vector spaces for damage classification in Structural Health Monitoring. Compared with traditional methods, this method does not require complex preprocessing steps and directly compares spectral/time-frequency structural information, resulting in improved performance.
COMPUTERS & STRUCTURES
(2022)
Article
Computer Science, Artificial Intelligence
Wei Luo, Yongqi Li, Fubin Yao, Shaokun Wang, Zhen Li, Peng Zhan, Xueqing Li
Summary: In this paper, we propose an efficient retrieval method for streaming time series data. Our method incrementally represents the features of streaming data to automatically prune dissimilar sequences and retain the most similar candidates for efficient one-pass searching. Extensive experiments on real world datasets demonstrate the superiority of our method.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Len Feremans, Boris Cule, Bart Goethals
Summary: Efficient and interpretable time series classification is crucial for many applications. This study proposes PETSC, a method that constructs an embedding based on sequential pattern occurrences and learns a linear model. PETSC outperforms baseline methods on both univariate and multivariate time series and scales well to large datasets.
DATA MINING AND KNOWLEDGE DISCOVERY
(2022)
Article
Computer Science, Artificial Intelligence
Jianping Gou, Xin He, Junyu Lu, Hongxing Ma, Weihua Ou, Yunhao Yuan
Summary: The researchers propose a novel CRC method called CMWCCR, which aims to enhance the discriminant representations among classes for better classification performance. The CMWCCR utilizes competitive, mean vector, and weighted constraints to learn discriminative class-specific representations, and the experimental results show its effectiveness and robustness.
Article
Computer Science, Artificial Intelligence
Shilei Hao, Zhihai Wang, Afanasiev D. Alexander, Jidong Yuan, Wei Zhang
Summary: Multivariate time series (MTS) classification is a growing field with increasing demand. Existing representation learning methods for MTS classification are limited in utilizing labels due to their reliance on self-supervised learning. To address this, a new Mixed Supervised Contrastive Loss (MSCL) is introduced for MTS representation learning, which combines self-supervised, intra-class, and inter-class supervised contrastive learning approaches. Based on MSCL, a novel Mixed supervised Contrastive learning framework for MTS classification (MICOS) is proposed, utilizing spatial and temporal channels to extract complex spatio-temporal features and applying MSCL at the timestamp level to capture multiscale contextual information. Experimental results on 30 public datasets from the UEA MTS archives demonstrate the reliability and efficiency of MICOS compared to 13 competitive baselines.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Moting Su, Wenjie Zhao, Ye Zhu, Donglan Zha, Yushu Zhang, Peng Xu
Summary: This paper aims to detect anomalous batteries by analyzing their internal resistance using time series analysis. The proposed method, PVT, encodes the sequential shapes of the batteries and generates a symbolic feature matrix. The results show that PVT significantly improves the performance of existing classifiers in detecting abnormal batteries.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yanting Li, Junwei Jin, C. L. Philip Chen
Summary: This paper proposes a new method for face recognition by incorporating unselected training samples into the modeling process, improving recognition accuracy and the effectiveness of classification.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Shuo Yang, Minjing Dong, Yunhe Wang, Chang Xu
Summary: In this article, a generative adversarial learning framework for time series imputation is proposed, which leverages the idea of conditional generative adversarial networks. The generator G, based on a modified bidirectional RNN structure, aims to generate missing values by utilizing temporal and nontemporal information extracted from the observed time series. The discriminator D is designed to differentiate between generated and real values in order to assist the generator in producing more authentic imputation results. Experimental results demonstrate significant improvements compared to state-of-the-art baseline models on various real-world time series datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Carlos A. Garcia, Edgar S. Garcia-Trevino, Noe Aguilar-Rivera, Cynthia Armendariz
JOURNAL OF CLEANER PRODUCTION
(2016)
Article
Computer Science, Information Systems
Edgar S. Garcia Trevino, Muhammad Zaid Hameed, Javier A. Barria
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2018)
Article
Biodiversity Conservation
Habacuc Flores-Moreno, Edgar S. Garcia-Trevino, Andrew D. Letten, Angela T. Moles
BIOLOGICAL INVASIONS
(2015)
Article
Computer Science, Interdisciplinary Applications
E. S. Garcia-Trevino, J. A. Barria
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2012)
Article
Computer Science, Interdisciplinary Applications
E. S. Garcia Trevino, V. Alarcon Aquino, J. A. Barria
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2019)
Article
Engineering, Civil
Suttipong Thajchayapong, Edgar S. Garcia-Trevino, Javier A. Barria
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2013)
Article
Computer Science, Information Systems
E. Juarez-Guerra, V. Alarcon-Aquino, P. Gomez-Gil, J. M. Ramirez-Cortes, E. S. Garcia-Trevino
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
P. Gomez-Gil, J. Rangel-Magdaleno, J. M. Ramirez-Cortes, E. Garcia-Trevino, I. Cruz-Vega
2016 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE PROCEEDINGS
(2016)